Systems and methods for detecting blink inhibition as a marker of engagement and perceived stimulus salience

ABSTRACT

The present systems and methods provide a mechanism to assess viewer behavior, features of stimuli, and the interaction between viewer behavior and stimuli. The systems and methods described herein for quantifying blink response and blink inhibition provide moment-by-moments measurements of viewer engagement by measuring what is or is not engaging enough to warrant viewers&#39; inhibition of blinking. The present disclosure describes measures of visual scanning, eye movements, blink data, and blink timing data to derive a measure of how engaged a person is with what he or she is looking at. Blink-related data as a measure of viewer engagement provides a mechanism for determining the most engaging spatial and temporal aspects of a stimulus.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a national stage entry of and claims the benefit ofand priority under 35 U.S.C. § 371 to International Application No.PCT/US2013/074487 entitled “Systems and Methods for Detecting BlinkInhibition as A Marker of Engagement and Perceived Stimulus Salience”filed on Dec. 11, 2013 and claims benefit under 35 U.S.C. § 119(e) ofU.S. Provisional Patent Application No. 61/735,865, filed Dec. 11, 2012,and entitled “Blink Inhibition as A Marker of Engagement and PerceivedStimulus Salience”, both of which are incorporated herein by referenceas if set forth herein in their entireties.

STATEMENT REGARDING FEDERALLY-SPONSORED RESEARCH

This invention was made with government support under grant numberP50-MH081756-01 awarded by the National Institute of Mental Health. Thegovernment has certain rights in the invention.

TECHNICAL FIELD

The present systems and methods relate generally to measuring eye-blinkbehavior and eye-blink inhibition as indicators of viewer engagementwith visual or auditory stimuli, and relate more particularly toutilizing the timing of blink inhibition during natural viewing to:assess viewer engagement with stimuli, to assess viewer perception ofthe relative salience of stimuli, to assess a stimulus's power to engagespecific viewers or groups of viewers, to identify the most engagingspatial and temporal features of a stimulus, and to categorize or rateviewers as a function of their engagement with a given stimulus fordemographic or diagnostic purposes.

BACKGROUND

When we blink, the flow of visual information between the world andone's retina is temporarily interrupted. In that instant of blinking,visual stimulation from the external world is lost for 150-400milliseconds (ms or msecs). As a result, the average adult in the courseof a single waking day will spend approximately 44 minutes with his orher eyelids closed missing visual information. During those moments, avariety of neural systems encompassing movement of the oculomotormuscles, activity in supplementary and frontal eye fields, andwidespread activity in visual, parietal, and prefrontal cortical areaswork together to suppress the actual visual signal of an occludingeyelid. These systems create the illusion of perceptual continuity, butif new visual information is presented in that instant of blinking, itwill be missed.

During the collection of eye movement data, eye-blinks have beentraditionally regarded as noise or artifact data and are generallydeemed useless. However, blinking also relates to cognitive statesbeyond mere physiological function. It is also generally known thatindividuals remain largely unaware of their blinking, although blinkingmay be generally related to both explicit and implicit attentionalpauses in task content.

Identification and quantification of a person's engagement with a visualstimulus can provide insights for many different fields. In cognitiveand behavioral testing, as for autism, attention deficit hyperactivitydisorder (ADHD), developmental disabilities, and other cognitiveconditions, measuring how engaged a viewer is with specific types ofvisual (or audible) content can provide a biomarker of disease/disorderstate, disease/disorder progression, and/or treatment response. Forinstance, children with developmental disabilities, which affect 1 in 10within the general population, show delayed acquisition of speech andlanguage skills. A measure of a child's engagement with speech andlanguage cues (e.g., level of engagement with talking faces orcommunication gestures, which are precursors to language acquisition)can aid in the diagnostic identification of a child with developmentaldisabilities at a much earlier age than diagnosis of such disabilitiesconventionally occurs.

In another example, in commercial industries, one of the main concernsfor many marketing companies is measuring the effectiveness of variousmarketing campaigns. Traditional approaches to determining visualmarketing campaign effectiveness include conducting consumer surveys andquestionnaires, analyzing sales numbers, social media “buzz”, etc.However, marketing companies would benefit from having a mechanism todetermine directly from viewer behavior, without secondhand reporting orsurveys, the effectiveness of a visual marketing campaign by measuringthe level of a viewer's or group of viewers' engagement to thatmarketing campaign during test trials prior to releasing the campaign orduring the actual campaign. In another example, developers of visualteaching aids may also benefit from having a measure of studentengagement levels during the development phase of the teaching aids.Other industries could benefit from measuring indicators of engagementto visual stimuli, such as video game developers, flying and drivingsimulator developers, etc.

Therefore, there is a long-felt but otherwise unresolved need for asystem and method that can assess and measure viewer engagement.Moreover, there is a need to measure engagement with certain visualand/or auditory stimuli, such as movies, television shows, marketingcampaigns, print ads, web pages, emergency videos, teaching aids, evenphysical environments and objects, etc. in order to enable optimizationthereof. Further, there is an additional need for a system and method touse measures of viewer engagement as biomarkers for assessingdisease/disorder state, disease/disorder progression, and/or treatmentresponse in conditions such as autism, ADHD, schizophrenia, bipolardisorder, depression, and others that effect engagement withcircumscribed content.

BRIEF SUMMARY OF THE DISCLOSURE

Briefly described, and according to one embodiment, aspects of thepresent disclosure generally relate to systems and methods for assessingblink inhibition and blink response as indicators of engagement withvisual stimuli. In particular, aspects of the present disclosure relateto utilizing the timing of blink inhibition during natural viewing andin response to visual stimuli to accomplish the following: to assessviewer engagement with stimuli, to assess viewer perception of therelative salience of stimuli, to assess a stimulus's power to engagespecific viewers or groups of viewers, to identify the most engagingspatial and temporal features of a stimulus, and to categorize or rateviewers as a function of their engagement with a given stimulus fordemographic or diagnostic purposes. According to one embodiment, thepresent systems and methods provide a tool for assessing viewerengagement on the basis of blink rate and the timing of blinking andblink inhibition, during natural viewing. In one embodiment, the presentsystems and methods provide a tool for quantifying viewers'moment-by-moment engagement with visual content and the degree to whichviewer engagement varies dynamically. In another embodiment, the presentsystems and methods provide a tool for quantifying listener'smoment-by-moment engagement with auditory content and the degree towhich listener engagement varies dramatically as it relates to blinkinhibition and blink data.

Further, and according to one embodiment, the present systems andmethods provide a mechanism for determining, by a “data mining”approach, the most engaging spatial and temporal features of a stimuluson the basis of time-varying viewer engagement. Further aspects of thepresent disclosure relate to the way in which these measures ofengagement can be combined with eye-tracking point-of-gaze data tomeasure the specific parts of a stimulus that a viewer is fixating uponat moments of greater or lesser engagement (e.g., fixation locations).

Further aspects of the present disclosure relate to systems and methodsfor assessing disease/disorder state (e.g., presence/absence of acondition), disease/disorder state progression, and/or treatmentresponse in conditions for example but not limited to autism spectrumdisorders (ASD), ADHD, schizophrenia, bipolar disorder, depression,post-traumatic stress disorder (PTSD), and others that effect engagementwith circumscribed content or engagement. In one embodiment, in researchleading to the present disclosure, toddlers with ASD, unlike typicallydeveloping comparison children, demonstrate markedly delayed blinkinhibition in relation to specific visual events. The present systemsand methods indicate that typical toddlers, relative to the same visualevents, inhibit their blinking earlier than toddlers with ASD. Thisdifference provides evidence of intact cognitive processes in typicaltoddlers and evidence that those processes are disrupted in toddlerswith ASD: typical toddlers inhibited their blinking in activeanticipation of the unfolding of salient social events, while toddlerswith ASD did not. These measurements provide information that can beused for assessing diagnostic status as well as for measuring severityof symptomatology. Related embodiments can be deployed to measure thelevel of engagement of, for example, recovering drug addicts withenvironmental triggers (for example, images of alcohol, drugs, orlocations in which such substances are typically procured or consumed)in order to assess risk for relapse.

In one embodiment, the present disclosure describes a method fordisplaying visual engagement over time of a plurality of individualswith respect to a dynamic visual stimulus. This embodiment includes thesteps receiving blink data indicative of blink responses to the dynamicvisual stimulus for each of the plurality of individuals; retrievingcontrol blink data from a database; comparing the received blink data tothe control blink data to identify one or more differences between thereceived blink data and the control blink data; and generating a displayof the one or more differences between the received blink data and thecontrol blink data. In certain embodiments, the above steps may beexecuted via software on a processor.

In one aspect, the method comprises blink data for each of the pluralityof individuals corresponding to a rate of change for each individual'spupil size and/or eyelid closure.

In one aspect, the method comprises the step of converting the blinkdata to binary format for comparison purposes and the step ofaggregating the blink data for the plurality of individuals. In certainembodiments, the above steps may be executed via software on aprocessor.

In one aspect, the method includes control blink data comprising anaverage blink rate for the plurality of individuals when no dynamicvisual stimulus is present, and/or an average blink rate for a group ofindividuals different from the plurality of individuals when no dynamicvisual stimulus is present, and/or a probability distribution of averageblink rates for the plurality of individuals as obtained by permutingthe blink data of the plurality of individuals.

In one aspect, the method comprises the step of permuting the data ofthe plurality of individuals comprises circular shifting with respect toan original timing of blink data collection and/or the step of permutingthe data of the plurality of individuals comprises randomizing an orderof blinks and inter-blink intervals for each individual.

In one aspect, the method comprises one or more differences between thereceived blink data and the control blink data comprising one or more ofthe following: increased blink rate as compared to the control blinkdata, decreased blink rate as compared to the control blink data, lackof blinks within a predetermined time period, exceeding a predeterminednumber of blinks within a predetermined time period and/or one or moredifferences between the received blink data and the control blink datais a marker of a developmental, cognitive, or mental disorder.

In one aspect, the method comprises the step of using the display of theone or more differences between the received blink data and the controlblink data in connection with a diagnosis of an individual, wherein theabove step may be executed via software on a processor.

In one aspect, the method further comprises the steps of synchronizingthe received blink data with the dynamic visual stimulus; and/orgenerating a display of the one or more differences between the receivedblink data and the control blink data in connection with the dynamicvisual stimulus. In certain embodiments, the above steps can be executedvia software on a processor.

In one embodiment, the present disclosure includes a method fordisplaying visual engagement over time of a plurality of individualswith respect to a stimulus. This embodiment includes the steps ofreceiving blink data indicative of blink responses to the stimulus foreach of the plurality of individuals; retrieving control blink data froma database; comparing the received blink data to the control blink datato identify one or more differences between the received blink data andthe control blink data; and generating a display of the one or moredifferences between the received blink data and the control blink data.In certain embodiments, the above steps can be executed via software ona processor.

In one aspect, the method includes the steps of: receiving eye-movementdata indicative of eye movements for each of the plurality ofindividuals with respect to the stimulus; determining from theeye-movement data a plurality of fixation locations with respect to thestimulus for the plurality of individuals; synchronizing the pluralityof fixation locations and the received blink data with the stimulus; andgenerating a display of the plurality of fixation locations at one ormore time points corresponding to the one or more differences betweenthe received blink data and the control blink data.

In one aspect, the method includes the display of the plurality offixation locations comprising a three-dimensional display, wherein twoof the dimensions correspond to the plurality of fixation locations foreach of the plurality of individuals and one of the dimensionscorresponds to time. Further, in one aspect, the method includes theplurality of fixation locations corresponding to each of the pluralityof individual's eye fixation locations with respect to one or moreframes of the stimulus. In another aspect, the method includes theplurality of fixation locations corresponding to point-of-gazecoordinate data for each of the plurality of individuals with respect tothe stimulus. In one aspect, the method further includes the display ofthe plurality of fixation locations comprising a three-dimensionalscanpath. According to one aspect, the method includes wherein the stepof synchronizing comprising time-locking or time-correlating theplurality of fixation locations to the received blink data.

In one aspect, the method comprises the blink data for each of theplurality of individuals corresponding to a rate of change for eachindividual's pupil size. According to one aspect, the method includesthe blink data for each of the plurality of individuals corresponding toeyelid closure.

In one aspect, the method further comprises the steps of converting theblink data to binary format for comparison purposes; and/or convertingthe eye-movement data to coordinate data for comparison purposes; and/oraggregating the blink data and the eye-movement data for the pluralityof individuals. In certain embodiments, the above steps can be executedvia software on a processor.

In one aspect, the method includes the control blink data comprising anaverage blink rate for the plurality of individuals when no stimulus ispresent. According to one aspect, the method includes the control blinkdata comprising an average blink rate for a group of individualsdifferent from the plurality of individuals when no stimulus is present.In one aspect, the control blink data comprising a probabilitydistribution of average blink rates for the plurality of individuals asobtained by permuting the blink data of the plurality of individuals.

In another aspect, the method includes the steps of permuting the dataof the plurality of individuals comprises circular shifting with respectto an original timing of blink data collection and/or permuting the dataof the plurality of individuals comprises randomizing an order of blinksand inter-blink intervals for each individual, wherein the steps may beexecuted via software on a processor.

In one aspect, the method includes one or more differences between thereceived blink data and the control blink data comprising one or more ofthe following: increased blink rate as compared to the control blinkdata, decreased blink rate as compared to the control blink data, lackof blinks within a predetermined time period, exceeding a predeterminednumber of blinks within a predetermined time period.

In one aspect, the method comprises the one or more differences betweenthe received blink data and the control blink data as a marker of adevelopmental, cognitive, or mental disorder.

In one aspect, the method includes the steps of using the display of theplurality of fixation locations at one or more time points correspondingto the one or more differences between the received blink data and thecontrol blink data in connection with a diagnosis of an individual;and/or synchronizing on the processor, the received blink data with thestimulus; and/or generating, a display of the one or more differencesbetween the received blink data and the control blink data in connectionwith the stimulus. In certain embodiments, the above steps can beexecuted via software on a processor.

In one aspect, the method includes the stimulus as an auditory stimulus,a dynamic visual stimulus, and/or a static visual stimulus. In anotheraspect, the method includes the stimulus comprising one or more of thefollowing: a dynamic stimulus, a dynamic visual stimulus, a pre-recordedvisual stimulus, a pre-recorded audio stimulus, a pre-recordedaudiovisual stimulus, a live visual stimulus, a live audio stimulus, alive audiovisual stimulus, a two-dimensional stimulus, or athree-dimensional stimulus.

In one embodiment, the present disclosure comprises a method fordetermining a measure of engagement by an individual with respect to adynamic visual stimulus. This embodiment includes the steps of:receiving blink data indicative of the individual's blink responses tothe dynamic visual stimulus; synchronizing the blink data with thedynamic visual stimulus; identifying a pattern of blink inhibition inthe synchronized blink data; and comparing the pattern of blinkinhibition in the synchronized blink data with the dynamic visualstimulus to identify a portion of the dynamic visual stimuluscontemporaneous with the pattern of blink inhibition, whereby thepattern of blink inhibition indicates a marker of engagement by theindividual with the contemporaneous portion of the dynamic visualstimulus. In certain embodiments, the above steps can be executed viasoftware on a processor.

In one aspect, the method includes the dynamic visual stimuluscomprising one or more of the following: a pre-recorded visual stimulus,a pre-recorded audiovisual stimulus, a live visual stimulus, a liveaudiovisual stimulus, a two-dimensional stimulus, or a three-dimensionalstimulus.

In one aspect, the method includes the pattern of blink inhibitioncomprising a mean blink rate for the individual during the dynamicvisual stimulus. In one aspect, the method further includes the patternof blink inhibition comprising a comparison between the individual'sblink data and a chance probability of blinking associated with theindividual. In another aspect, the method includes the pattern of blinkinhibition comprising a moment-by-moment blink rate for the individual.According to one aspect, the method includes the pattern of blinkinhibition comprising a measure of an instantaneous blink rate for theindividual at a certain time point as compared to a mean blink rate forthe individual. In one aspect, the method includes the pattern of blinkinhibition comprising a measure of an instantaneous blink rate for theindividual at a certain time point as compared to a mean blink rate fora control group.

In one aspect, the method includes the pattern of blink inhibitioncomprising a measure of an instantaneous blink rate for the individualas compared to measure of variance in a mean blink rate for theindividual. In another aspect, the method includes the pattern of blinkinhibition comprising a measure of the synchronized blink data ascompared with control blink data. In one aspect, the method includes thepattern of blink inhibition comprising a measure of blink inhibitionrelative to an event in the dynamic visual stimulus.

In one aspect, the contemporaneous portion of the dynamic visualstimulus comprises the entirety of the dynamic visual stimulus.According to one aspect, the method includes the event in the dynamicvisual stimulus comprising a physical event or an affective event.

In one aspect, the method includes the step of categorizing theindividual into one or more predefined categories based on the marker ofengagement. In another aspect, the marker of engagement relates to asalient portion of the dynamic visual stimulus.

According to one aspect, the method includes the step of synchronizing,which comprises time-locking or time-correlating the blink data with thedynamic visual stimulus.

In one aspect, the method includes the blink data corresponding to arate of change of the individual's pupil size. In another aspect, themethod includes the blink data corresponding to eyelid closure of theindividual.

In another aspect, the method further comprises the steps of converting,via software executing on the processor, the blink data to binary formatfor comparison purposes and/or categorizing, via software executing onthe processor, the blink data according to predetermined demographicparameters.

In one embodiment, the present disclosure comprises a method fordetermining a measure of engagement by an individual with respect to astimulus. This embodiment includes the steps of: receiving blink dataindicative of the individual's blink responses to the stimulus;synchronizing the received blink data with the stimulus; identifying,via software executing on the processor, a pattern of blink inhibitionin the synchronized blink data; and comparing the pattern of blinkinhibition in the synchronized blink data with the stimulus to identifya portion of the stimulus contemporaneous with the pattern of blinkinhibition, whereby the pattern of blink inhibition indicates a markerof engagement by the individual with the contemporaneous portion of thestimulus. In certain embodiments, the above steps can be performed viasoftware on a processor.

In one aspect, the method includes the steps of receiving eye-movementdata indicative of the individual's eye movements with respect to thestimulus; determining from the eye-movement data a plurality of fixationlocations with respect to the stimulus; and comparing the plurality offixation locations with the stimulus at the contemporaneous portion ofthe stimulus. In certain embodiments, the above steps can be executedvia software on a processor.

According to one aspect, the method includes the pattern of blinkinhibition comprising a comparison between the individual's blink dataand a chance probability of blinking associated with the individual. Inone aspect, the method includes the stimulus comprising one or more ofthe following: a dynamic stimulus, a dynamic visual stimulus, apre-recorded visual stimulus, a pre-recorded audio stimulus, apre-recorded audiovisual stimulus, a live visual stimulus, a live audiostimulus, a live audiovisual stimulus, a two-dimensional visual oraudiovisual stimulus, or a three-dimensional visual or audiovisualstimulus.

In one aspect, the method includes the pattern of blink inhibitioncomprising a mean blink rate for the individual during the stimulus.According to one aspect, the method includes the pattern of blinkinhibition comprising a moment-by-moment blink rate for the individual.In one aspect, the method includes the pattern of blink inhibitioncomprising a measure of an instantaneous blink rate for the individualat a certain time point as compared to a mean blink rate for theindividual. In another aspect, the present method includes the patternof blink inhibition comprising a measure of an instantaneous blink ratefor the individual at a certain time point as compared to a mean blinkrate for a control group. According to one aspect, the method includesthe pattern of blink inhibition comprising a measure of the synchronizedblink data as compared with predetermined control blink data. In yetanother aspect, the present method includes the pattern of blinkinhibition comprising a measure of blink inhibition relative to an eventin the stimulus.

In one aspect, the method includes the contemporaneous portion of thestimulus comprising the entirety of the stimulus. In another aspect, themethod includes the event in the stimulus comprising a physical event oran affective event.

According to one aspect, the method comprises the steps of categorizingthe individual into one or more predefined categories based on themarker of engagement and/or synchronizing comprises time-locking ortime-correlating the blink data with the stimulus.

In one aspect, the method comprises the marker of engagement relating toa salient portion of the stimulus.

In one aspect, the method further includes the blink data corresponds toa rate of change of the individual's pupil size. According to oneaspect, the method comprises the blink data corresponds to eyelidclosure of the individual.

In one aspect, the method further comprises the steps of converting theblink data to binary format for comparison purposes and/or categorizingthe blink data according to predetermined demographic parameters. Incertain embodiments, the above steps can be executed via software on aprocessor.

In one aspect, the method includes a stimulus comprising an auditorystimulus, dynamic visual stimulus, and/or static stimulus.

In one embodiment, the present disclosure comprises a method fordetermining perceived stimulus salience by an individual with respect toa stimulus. This embodiment comprises the steps of: receiving blink dataindicative of the individual's blink responses to the stimulus;receiving eye-movement data indicative of eye movements for theindividual with respect to the stimulus; synchronizing the receivedblink data and the received eye-movement data with the stimulus;identifying a period of blink inhibition in the synchronized blink data;and determining for the period of blink inhibition identified in thesynchronized blink data, at least one spatial fixation location from thesynchronized eye-movement data for the individual with respect to thestimulus, whereby the period of blink inhibition and the at least onespatial fixation location indicate markers of perceived temporal andspatial salience with respect to the stimulus. In certain embodiments,the above steps can be executed via software on a processor.

In one aspect, the method includes the step of synchronizing, whichcomprises time-locking or time-correlating the received blink data andthe received eye-movement data with the stimulus.

In one aspect, the method further comprises the steps of converting theblink data to binary format for determination purposes; and/orconverting the eye-movement data to coordinate data for determinationpurposes. In certain embodiments, the above steps can be executed viasoftware on a processor.

According to one aspect, the method further comprises the step ofidentifying the period of blink inhibition in the synchronized blinkdata further including the steps of: retrieving control blink data froma database; and comparing the synchronized blink data to the controlblink data to identify a difference between the synchronized blink dataand the control blink data, whereby the difference corresponds to theperiod of blink inhibition. In certain embodiments, the above steps canbe executed via software on a processor.

In one aspect, the present method includes the control blink datacomprising an average blink rate for a plurality of individuals when nostimulus is present. In one aspect, the present method includes thedifference between the synchronized blink data and the control blinkdata comprising one of the following: increased blink rate for theindividual as compared to the control blink data, decreased blink ratefor the individual as compared to the control blink data, lack of blinkswithin a predetermined time period, exceeding a predetermined number ofblinks within a predetermined time period.

According to one aspect, the present method comprises the differencebetween the synchronized blink data and the control blink data providesa marker of a developmental, cognitive, or mental disorder of theindividual. In one embodiment, the present method includes the blinkdata corresponding to a blink rate for the individual during a definedtime period.

According to one aspect, the present method includes the stimuluscomprising an auditory stimulus, a dynamic visual stimulus, and/or astatic visual stimulus. In one aspect, the present method includes thestimulus comprising one or more of the following: a pre-recorded visualstimulus, a pre-recorded audio stimulus, a pre-recorded audiovisualstimulus, a live visual stimulus, a live audio stimulus, a liveaudiovisual stimulus, a two-dimensional stimulus, or a three-dimensionalstimulus.

In one embodiment, the present disclosure comprises a method forassessing an ability of a stimulus to engage an individual, comprisingthe steps of: presenting the stimulus to an individual; receiving blinkdata indicative of the individual's blink responses to the stimulus;identifying a measure of blink inhibition for the individual from thereceived blink data; and determining, via software executing on theprocessor, whether the measure of blink inhibition in the received blinkdata meets a threshold blink inhibition value, whereby the thresholdblink inhibition value indicates the ability of the stimulus to engagethe individual. In certain embodiments, the above steps can be executedvia software on a processor.

In one aspect, the present method includes the step of categorizing, viasoftware executing on the processor, the blink data according topredetermined demographic parameters.

In one aspect, the present method includes the stimulus comprising oneor more of the following: a dynamic stimulus, a dynamic visual stimulus,a pre-recorded visual stimulus, a pre-recorded audio stimulus, apre-recorded audiovisual stimulus, a live visual stimulus, a live audiostimulus, a live audiovisual stimulus, a two-dimensional stimulus, or athree-dimensional stimulus.

According to one aspect, the present method includes the measure ofblink inhibition comprising a mean blink rate for the individual duringthe stimulus. In one aspect, the present method includes the measure ofblink inhibition comprising a moment-by-moment blink rate for theindividual. In one aspect, the present method includes the measure ofblink inhibition comprising a measure of an instantaneous blink rate forthe individual at a certain time point as compared to a mean blink ratefor the individual, a measure of an instantaneous blink rate for theindividual at a certain time point as compared to a mean blink rate fora control group, a measure of the received blink data as compared withpredetermined control blink data, and/or a measure of blink inhibitionrelative to an event in the stimulus.

In one aspect, the present method includes the event in the stimuluscomprising a physical event or an affective event.

In one aspect, the present method includes the blink data correspondingto a rate of change of the individual's pupil size and/or eyelid closureof the individual.

According to one aspect of the present method, the method furthercomprises the steps of, via software executing on the processor,converting the blink data to binary format for determination purposes,categorizing the blink data according to predetermined demographicparameters.

In one aspect, the present method comprises the threshold blinkinhibition value indicates a marker for a mental condition. In anotheraspect, the present method includes the threshold blink inhibition valueas selected from a range spanning normality to psychopathology. In oneaspect, the present method includes the threshold blink inhibition valuecorresponding to a diagnostic measure for diagnosing the individual witha mental condition. In one aspect, the present method includes thethreshold blink inhibition value corresponding to a predeterminedmeasure of engagement with the stimulus. In another aspect, the presentmethod further includes the threshold blink inhibition valuecorresponding to a predetermined category for categorizing theindividual.

In one aspect, the present method includes the measure of blinkinhibition for the individual corresponding to a portion of thestimulus. In another aspect, the present method includes the measure ofblink inhibition for the individual corresponding to an entirety of thestimulus.

According to one aspect, the present method includes the stimulus as anauditory stimulus, a dynamic visual stimulus, and/or a static visualstimulus.

In one embodiment, the present disclosure comprises a method forassessing the risk of a mental condition in an individual using an eyemonitoring device. This embodiment comprises the steps of: receivingblink data indicative of the individual's blink responses to a dynamicvisual stimulus displayed to the individual, wherein the blink data iscollected via the eye monitoring device; synchronizing the receivedblink data with the dynamic visual stimulus; identifying a pattern ofblink inhibition in the synchronized blink data; retrieving event datarelated to the dynamic visual stimulus from a database; and comparing aparameter of the pattern of blink inhibition in the synchronized blinkdata with a parameter of the event data related to the dynamic visualstimulus to determine at least one delta parameter, wherein the at leastone delta parameter indicates a likelihood that the individual has amental disorder. In certain embodiments, the above steps can be executedvia software on a processor.

In one aspect, the present method includes the parameter of the eventdata comprising a predetermined time-stamped event. In another aspect,the present method includes the event data comprising a time value.

In one aspect, the present method includes the parameter of the patternof blink inhibition comprising a time value. In one aspect, the presentmethod includes the pattern of blink inhibition comprising a comparisonbetween the individual's blink data and a chance probability of blinkingassociated with the individual. In another aspect, the present methodincludes the pattern of blink inhibition comprising a mean blink ratefor the individual during the dynamic visual stimulus. Further, in oneaspect, the present method includes the pattern of blink inhibitioncomprising a moment-by-moment blink rate for the individual. In oneaspect, the present method includes the pattern of blink inhibitioncomprising a measure of an instantaneous blink rate for the individualat a certain time point as compared to a mean blink rate for theindividual. In yet another aspect, the present method includes thepattern of blink inhibition comprising a measure of an instantaneousblink rate for the individual at a certain time point as compared to amean blink rate for a control group. In one aspect, the present methodincludes the pattern of blink inhibition comprising a measure of aninstantaneous blink rate for the individual as compared to measure ofvariance in a mean blink rate for the individual.

In one aspect, the present method includes the at least one deltaparameter comprising a time value that exceeds a predetermined thresholdvalue. In one aspect, the present method includes the at least one deltaparameter comprising a time value that is less than a predeterminedthreshold value.

In one aspect, the present method comprises the steps of providing adiagnosis to the individual based on the at least one delta parameterand/or synchronizing, which comprises time-locking or time-correlatingthe received blink data with the dynamic visual stimulus.

In one aspect, the present method includes the mental conditioncomprising a developmental or cognitive disorder.

In one aspect, the present method includes the event data correspondingto one or more of the following: physical events within the dynamicvisual stimulus, affective events within the dynamic visual stimulus,events presumed to cause or inhibit blinking based on the dynamic visualstimulus.

In one embodiment, the present disclosure comprises A method forevaluating, monitoring, or diagnosing a mental disorder in an individualusing an eye monitoring device. This embodiment comprises the steps of:receiving blink data indicative of the individual's blink responses to astimulus, wherein the blink data is collected via the eye monitoringdevice; synchronizing the received blink data with the stimulus;identifying, via software executing on the processor, a pattern of blinkinhibition in the synchronized blink data; retrieving event data relatedto the visual stimulus from a database; and comparing a parameter of thepattern of blink inhibition in the synchronized blink data with aparameter of the event data related to the visual stimulus to determinea delta parameter, wherein the delta parameter indicates a likelihoodthat the individual has a mental disorder. In certain embodiments, theabove steps can be executed via software on a processor.

In one aspect, the present method further comprises the steps of:receiving eye-movement data indicative of the individual's eye movementswith respect to the stimulus; receiving eye-movement data indicative ofeach member of a control group's eye movements with respect to thestimulus; generating a three-dimensional scanpath based on the data foreach of the members of the control group and for the individual, whereintwo of the dimensions of the scanpath correspond to a position of apoint of regard for each of the members and the individual and one ofthe dimensions corresponds to time; identifying a convergence of thescanpaths of the members of the control group; and comparing viasoftware executing on the processor, the scanpath of the individual tothe scanpaths of the members of the control group in the region of theconvergence. In certain embodiments, the above steps can be executed viasoftware on a processor.

In one aspect, the present method includes wherein the parameter of theevent data comprising a predetermined time-stamped event. In one aspect,the present method includes the parameter of the event data comprising atime value. In another aspect, the present method includes the parameterof the pattern of blink inhibition comprising a time value.

In one aspect, the present method includes the delta parametercomprising a time value that exceeds a predetermined threshold value. Inone aspect, the present method includes the delta parameter comprising atime value that is less than a predetermined threshold value.

In one aspect, the present method includes the stimulus as an auditorystimulus, a dynamic visual stimulus, and/or a static visual stimulus.

In one aspect, the present method includes the event data correspondingto one or more of the following: physical events within the dynamicvisual stimulus, affective events within the dynamic visual stimulus,events presumed to cause or inhibit blinking based on the dynamic visualstimulus.

In one aspect, the present method includes the step of synchronizingwhich comprises time-locking or time-correlating the received blink datawith the stimulus.

In one aspect, the present method includes the pattern of blinkinhibition comprising a comparison between the individual's blink dataand a chance probability of blinking associated with the individual. Inone aspect, the present method includes the pattern of blink inhibitioncomprising a mean blink rate for the individual during the stimulus. Inanother aspect, the present method includes the pattern of blinkinhibition comprising a moment-by-moment blink rate for the individual.In one aspect, the present method includes the pattern of blinkinhibition comprising a measure of an instantaneous blink rate for theindividual at a certain time point as compared to a mean blink rate forthe individual. In yet another aspect, the present method includes thepattern of blink inhibition comprising a measure of an instantaneousblink rate for the individual at a certain time point as compared to amean blink rate for a control group. Further, in another aspect, thepresent method includes the pattern of blink inhibition comprising ameasure of an instantaneous blink rate for the individual as compared tomeasure of variance in a mean blink rate for the individual.

In one embodiment, the present disclosure comprises a method forevaluating, monitoring, or diagnosing a mental condition in anindividual using an eye monitoring device. This embodiment comprises thesteps of: receiving blink data indicative of the individual's blinkresponses to a dynamic visual stimulus displayed to the individual,wherein the blink data is collected using the eye monitoring device;synchronizing the received blink data with the dynamic visual stimulus;identifying a pattern of blink inhibition in the synchronized blinkdata; retrieving a control pattern of blink inhibition for the dynamicvisual stimulus displayed to the individual from a database; andcomparing the pattern of blink inhibition in the synchronized blink datawith the control pattern of blink inhibition to determine whether thepattern of blink inhibition falls outside a range of the control patternof blink inhibition and thereby indicates a likelihood that theindividual has a mental condition. In certain embodiments, the abovesteps can be executed via software on a processor.

In one aspect, the present method includes the mental conditioncomprising a developmental or cognitive disorder.

In one aspect, the present method further includes the steps ofsynchronizing comprises time-locking or time-correlating the receivedblink data with the dynamic visual stimulus and/or converting, viasoftware executing on the processor, the blink data to binary format foridentification purposes.

In one aspect, the present method includes the blink data for theindividual corresponding to a rate of change of pupil size for theindividual. In one aspect, the present method includes the blink datacorresponding to eyelid closure for the individual.

In one aspect, the present method includes the control pattern of blinkinhibition comprising an average blink rate for a plurality ofindividuals in response to the dynamic visual stimulus. According to oneaspect, the present method includes the control pattern of blinkinhibition comprising a probability distribution of average blink ratesfor a plurality of individuals as obtained by permuting the blink dataof the plurality of individuals. According to one aspect, the presentmethod includes the control pattern of blink inhibition indicating aseverity of the mental condition.

In one aspect, the present method includes the steps of permuting thedata of the plurality of individuals comprising circular shifting withrespect to an original timing of blink data collection and/or the stepof permuting the data of the plurality of individuals comprisingrandomizing an order of blinks and inter-blink intervals for eachindividual.

In one aspect, the present method includes the pattern of blinkinhibition comprising a mean blink rate for the individual during thedynamic visual stimulus. In another aspect, the present method includesthe pattern of blink inhibition comprising a moment-by-moment blink ratefor the individual. In one aspect, the present method includes thepattern of blink inhibition comprising a measure of an instantaneousblink rate for the individual at a certain time point and the controlpattern of blink inhibition comprises an instantaneous blink rate for acontrol group. In one aspect, the present method includes the controlpattern of blink inhibition comprising an average blink rate for theindividual when no dynamic visual stimulus is present. In yet anotheraspect, the present method includes the pattern of blink inhibitioncomprising a measure of blink inhibition relative to an event in thedynamic visual stimulus.

In one aspect, the present method includes the event in the dynamicvisual stimulus comprising a physical event or an affective event.

In one embodiment, the present disclosure comprises a method forassessing user responses to a stimulus based on blink inhibition. Thisembodiment comprises the steps of: receiving blink data indicative of auser's blink responses to a stimulus; identifying a pattern of blinkinhibition in the blink data; retrieving a control pattern of blinkinhibition for the stimulus from a database, wherein the control patterncorresponds to a predefined user category; and comparing the pattern ofblink inhibition in the blink data with the control pattern of blinkinhibition to determine whether the user within the predefined usercategory. In certain embodiments, the above steps can be executed viasoftware on a processor.

In one aspect, the present method includes the blink data is receivedvia the use of an eye monitoring device.

In one aspect, the present method includes the stimulus comprising anauditory stimulus, a dynamic visual stimulus, and/or a static visualstimulus. In one aspect, the present method includes the stimuluscomprising one or more of the following: a dynamic stimulus, a dynamicvisual stimulus, a pre-recorded visual stimulus, a pre-recorded audiostimulus, a pre-recorded audiovisual stimulus, a live visual stimulus, alive audio stimulus, a live audiovisual stimulus, a two-dimensionalstimulus, or a three-dimensional stimulus.

In one aspect, the present method includes the blink data for the usercorresponding to a rate of change of pupil size for the user. In anotheraspect, the present method comprises the blink data corresponds toeyelid closure for the user.

In one aspect, the present method includes the control pattern of blinkinhibition comprising an average blink rate for a plurality of users inresponse to the stimulus. In one aspect, the present method includes thecontrol pattern of blink inhibition comprising a probabilitydistribution of average blink rates for a plurality of users as obtainedby permuting the blink data of the plurality of users.

In one aspect, the present method includes the steps of converting, viasoftware executing on the processor, the blink data to binary format forcomparison purposes, permuting the data of the plurality of userscomprising circular shifting with respect to an original timing of blinkdata collection, and/or permuting the data of the plurality of userscomprises randomizing an order of blinks and inter-blink intervals foreach user.

In one aspect, the present method includes the pattern of blinkinhibition comprising a mean blink rate for the user during thestimulus. In another aspect, the present method includes the pattern ofblink inhibition comprising a moment-by-moment blink rate for the user.In one aspect, the present method includes the pattern of blinkinhibition comprising a measure of an instantaneous blink rate for theuser at a certain time point and the control pattern of blink inhibitioncomprising an instantaneous blink rate for a control group. In anotheraspect, the present method includes the pattern of blink inhibitioncomprising a measure of blink inhibition relative to an event in thestimulus.

In one aspect, the present method includes the control pattern of blinkinhibition comprising an average blink rate for the user when nostimulus is present.

In one aspect, the present method includes wherein the event in thedynamic stimulus comprising a physical event or an affective event.

In one aspect, the present method includes the steps of receivingadditional blink data for the user over time; identifying an additionalpattern of blink inhibition in the additional blink data; comparing theadditional pattern of blink inhibition to the pattern of blinkinhibition to determine whether the user remains within thepredetermined user category. In certain embodiments, the above steps canbe executed via software on a processor.

These and other aspects, features, and benefits of the claimedinvention(s) will become apparent from the following detailed writtendescription of the preferred embodiments and aspects taken inconjunction with the following drawings, although variations andmodifications thereto may be effected without departing from the spiritand scope of the novel concepts of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate one or more embodiments and/oraspects of the disclosure and, together with the written description,serve to explain the principles of the disclosure. Wherever possible,the same reference numbers are used throughout the drawings to refer tothe same or like elements of an embodiment, and wherein:

FIG. 1A illustrates an exemplary block diagram of an eye monitoringsystem, according to one embodiment of the present disclosure.

FIG. 1B illustrates data eye movement responses to a visual stimulus,according to one aspect of the present disclosure.

FIG. 1C illustrates a display of portions of a dynamic visual stimulusover time and data indicative of eye movement responses to the dynamicvisual stimulus, according to one embodiment of the present disclosure.

FIG. 1D shows an exemplary generation of display of a group'sdistribution of visual resources, according to one embodiment of thepresent disclosure.

FIG. 2 is a flowchart showing an overview of the data collection andassessment process of an eye monitoring system, according to oneembodiment of the present disclosure.

FIG. 3 illustrates blinking and statistically significant blinkinhibition while viewing a visual stimulus, according to one embodimentof the present disclosure.

FIG. 4 is a graph illustrating an exemplary blink rate comparisonbetween typical toddlers and toddlers with autism spectrum disorder,according to one embodiment of the present disclosure.

FIG. 5A is a graph illustrating an exemplary correlation of blink ratesand age in typical toddlers, according to one aspect of the presentdisclosure.

FIG. 5B is a graph illustrating an exemplary correlation of blink ratesand age in toddlers diagnosed with autism spectrum disorder, accordingto one aspect of the present disclosure.

FIGS. 6A and 6B illustrate task dependent modulation of blink rate oftypical toddlers and toddlers with ASD, according to one embodiment ofthe present disclosure.

FIGS. 6C, 6D, and 6E illustrate task dependent modulation of blink ratebetween two different viewer groups, according to one embodiment of thepresent disclosure.

FIG. 7A is a graph illustrating participant blink data versus time,according to one embodiment of the present disclosure.

FIG. 7B is a graph illustrating instantaneous blink rate versus time,according to one embodiment of the present disclosure.

FIG. 7C is a graph illustrating the 95^(th) and 5^(th) percentile ofpermuted blink data versus time, according to one embodiment of thepresent disclosure.

FIG. 7D is a graph illustrating periods of blink inhibition plotted withrespect to time, according to one embodiment of the present disclosure.

FIG. 8A is a graph illustrating blink inhibition relative to affectiveevents for typical toddlers, according to one embodiment of the presentdisclosure.

FIG. 8B is a graph illustrating blink inhibition relative to physicalevents for typical toddlers, according to one embodiment of the presentdisclosure.

FIG. 8C is a graph illustrating blink response relative to nonaffectiveand nonphysical events for typical toddlers, according to one embodimentof the present disclosure.

FIG. 8D illustrates exemplary visual fixation relating to affectiveevents for typical toddlers, according to one embodiment of the presentdisclosure.

FIG. 8E illustrates exemplary visual fixation relating to physicalevents for typical toddlers, according to one embodiment of the presentdisclosure.

FIG. 8F illustrates exemplary visual fixation relating to nonaffectiveand nonphysical events for typical toddlers, according to one embodimentof the present disclosure.

FIG. 8G is a graph illustrating blink inhibition relative to affectiveevents for toddlers diagnosed with autism spectrum disorder, accordingto one embodiment of the present disclosure.

FIG. 8H is a graph illustrating blink inhibition relative to physicalevents for toddlers diagnosed with autism spectrum disorder, accordingto one embodiment of the present disclosure.

FIG. 8I is a graph illustrating blink response relative to non-affectiveand nonphysical events for toddlers diagnosed with autism spectrumdisorder, according to one embodiment of the present disclosure.

FIG. 8J illustrates exemplary visual fixation relating to affectiveevents for toddlers with autism spectrum disorder, according to oneembodiment of the present disclosure.

FIG. 8K illustrates exemplary visual fixation relating to physicalevents for toddlers with autism spectrum disorder, according to oneembodiment of the present disclosure.

FIG. 8L illustrates exemplary visual fixation relating to nonaffectiveand nonphysical events for toddlers with autism spectrum disorder,according to one embodiment of the present disclosure.

FIG. 8M is a graph illustrating timing of blink inhibition relative toaffective and physical events for toddlers diagnosed with autismspectrum disorder and typical toddlers, according to one embodiment ofthe present disclosure.

FIG. 8N is a graph illustrating percent change in blinks per minuterelative to affective physical and events for toddlers diagnosed withautism spectrum disorder and typical toddlers, according to oneembodiment of the present disclosure.

FIG. 8O is a graph illustrating percent fixation on objects relative toaffective and physical events for toddlers diagnosed with autismspectrum disorder and typical toddlers, according to one embodiment ofthe present disclosure.

FIG. 9A is a graph illustrating an empirical cumulative distributionfunction comparing actual typical toddler data with permuted typicaltoddler data, according to one embodiment of the present disclosure.

FIG. 9B is a graph illustrating an empirical cumulative distributionfunction comparing actual toddlers diagnosed with ASD data with permutedtoddlers diagnosed with ASD data, according to one aspect of the presentdisclosure.

FIG. 10 is a flowchart illustrating an exemplary process for determiningengagement activity, according to one embodiment of the presentdisclosure.

FIG. 11 is a flowchart illustrating an exemplary process for assessingdisease/disorder state, according to one embodiment of the presentdisclosure.

FIG. 12 is a flowchart illustrating an exemplary process for assessingperceived stimulus salience, according to one embodiment of the presentdisclosure.

FIG. 13 is a flowchart illustrating an exemplary process for identifyingmost engaging spatial and temporal features of a visual stimulus,according to one embodiment of the present disclosure.

FIG. 14 is a flowchart illustrating an exemplary patient/conditionassessment process, according to one embodiment of the presentdisclosure.

DETAILED DESCRIPTION

Prior to a detailed description of the disclosure, the followingdefinitions are provided as an aid to understanding the subject matterand terminology of aspects of the present systems and methods, areexemplary, and not necessarily limiting of the aspects of the systemsand methods, which are expressed in the claims. Whether or not a term iscapitalized is not considered definitive or limiting of the meaning of aterm. As used in this document, a capitalized term shall have the samemeaning as an uncapitalized term, unless the context of the usagespecifically indicates that a more restrictive meaning for thecapitalized term is intended. However, the capitalization or lackthereof within the remainder of this document is not intended to benecessarily limiting unless the context clearly indicates that suchlimitation is intended.

All publications, patents, and published patent applications referred toin this application are specifically incorporated by reference herein.In case of conflict, the present specification, including its specificdefinitions, will control.

Throughout this specification, the term “comprise” or variations such as“comprising” or “comprises” will be understood to imply the inclusion ofa stated integer (or component) or group of integers (or components),but not the exclusion of any integer (or component) or group of integers(or components).

The singular forms “a”, “an”, and “the” include the plurals unless thecontext clearly dictates otherwise.

Definitions/Glossary

M: mean or average of a set of numerical values within a data set.

SD: standard deviation, which indicates the variation from an average ormean value of a relevant data set.

r: Pearson's product-moment correlation coefficient, which is a measureof the strength and direction of the linear relationship between twovariables, normally within a related data set.

t: test statistic value from 1 or 2 sample t test.

P: is a symbol for percentage/percentile of some set of data points.

ANOVA: analysis of variance, which is a collection of statistical modelsused to analyze the differences between group means (averages) andassociated variations of data among and between groups.

SE: Standard error, which is the standard deviation of the samplingdistribution of a statistic.

F: f-test, which is a statistical test where the test statistic has anF-distribution under the null hypothesis, mostly used when comparingstatistical model that fit into a data set.

z: the result of a z-test that is a statistical test for which thedistribution of the test statistic under the null hypothesis can beapproximated by a normal distribution within a given data set.

Overview

For the purpose of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the embodimentsillustrated in the drawings and specific language will be used todescribe the same. It will, nevertheless, be understood that nolimitation of the scope of the disclosure is thereby intended; anyalterations and further modifications of the described or illustratedembodiments, and any further applications of the principles of thedisclosure as illustrated therein are contemplated as would normallyoccur to one skilled in the art to which the disclosure relates. Alllimitations of scope should be determined in accordance with and asexpressed in the claims.

Aspects of the present disclosure generally relate to systems andmethods for assessing blink inhibition and blink response as indicatorsof engagement with visual stimuli. In particular, aspects of the presentdisclosure relate to utilizing the timing of blink inhibition duringnatural viewing and in response to visual stimuli to: to assess viewerengagement with stimuli, to assess viewer perception of the relativesalience of stimuli, to assess a stimulus's power to engage specificviewers or groups of viewers, to identify the most engaging spatial andtemporal features of a stimulus, and to categorize or rate viewers as afunction of their engagement with a given stimulus for demographic ordiagnostic purposes. According to one embodiment, the present systemsand methods provide a tool for assessing viewer engagement on the basisof blink rate and the timing of blinking and blink inhibition, duringnatural viewing. In one embodiment, the present systems and methodsprovide a tool for quantifying viewers' moment-by-moment engagement withvisual content and the degree to which viewer engagement variesdynamically.

Further, and according to one embodiment, the present systems andmethods provide a mechanism for determining, by a “data mining”approach, the most engaging spatial and temporal features of a stimuluson the basis of time-varying viewer engagement. Further aspects of thepresent disclosure relate to the way in which these measures ofengagement can be combined with eye-tracking point-of-gaze data tomeasure the specific parts of a stimulus that a viewer is fixating uponat moments of greater or lesser engagement (e.g., fixation locations).

Further aspects of the present disclosure relate to systems and methodsfor assessing disease/disorder state (e.g., presence/absence of acondition), disease/disorder state progression, and/or treatmentresponse in conditions for example but not limited to autism spectrumdisorders (ASD), ADHD, schizophrenia, bipolar disorder, depression,post-traumatic stress disorder (PTSD), and others that effect engagementwith circumscribed content or engagement. In one embodiment, in researchleading to the present disclosure, toddlers with ASD, unlike typicallydeveloping comparison children, demonstrate markedly delayed blinkinhibition in relation to specific visual events. The present systemsand methods indicate that typical toddlers, relative to the same visualevents, inhibit their blinking earlier than toddlers with ASD. Thisdifference provides evidence of intact cognitive processes in typicaltoddlers and evidence that those processes are disrupted in toddlerswith ASD: typical toddlers inhibited their blinking in activeanticipation of the unfolding of salient social events, while toddlerswith ASD did not. These measurements provide information that can beused for assessing diagnostic status as well as for measuring severityof symptomatology. Related embodiments can be deployed to measure thelevel of engagement of, for example, recovering drug addicts withenvironmental triggers (for example, images of alcohol, drugs, orlocations in which such substances are typically procured or consumed)in order to assess risk for relapse.

Experimental Data and Analysis

The following exemplary discussion relates to a conducted experiment(s)to measure blink inhibition as an indicator of viewer engagement withvisual stimuli. The experiment(s) utilize the timing of blink inhibitionof toddlers during natural viewing of a stimulus to assess variousaspects in connection with levels of engagement. Details of theexperiment(s) conducted along with associated data/parameters, exemplarysettings, the associated results from the experiment(s), generalimplications, and alternate embodiments will be better understood in thedescription and accompanying figures provided in greater detail below.

In the descriptions that follow, the term “blink data” generally relatesto a measurement of the timing and/or presence/number of eye-blinksduring natural viewing of a visual stimulus, how blinks are modulatedbetween as well as within tasks, and how the timing of blinks varies asa function of viewer engagement and various stimulus events. Additionalexamples of blink data may comprise blink rate before, during, and aftera task, wherein the blink rate is measured and analyzed for variationsat a plurality of intervals throughout the viewing of a visual stimulus;in particular, before, during, and after viewing the visual stimulus.Blink data may also comprise measurements of instantaneous blink rate asit relates to intratask blink inhibition. Further, it will be understoodthat blink data are generally used to assess various levels of blinkinhibition, timing of blink inhibition, viewer engagement with stimuli,viewer perception of relative salience of stimuli, a stimuli'scapability to engage a viewer, etc. In another aspect, blink data mayrelate to the measurement of the timing and/or presence/number ofeye-blinks during the listening of an auditory stimulus. Similar to thevarious mechanisms relating to a visual stimulus, the same measures mayapply while listening to an auditory stimulus.

Also referred to herein, stimulus events in this example, withoutlimitation to other possible embodiments thereof, generally comprisethree groups: nonaffective/nonphysical events, physical events, andaffective events. Affective events generally comprise events within thevisual stimulus having an effect on emotional behavior, such as facialexpressions and/or vocalizations eliciting heightened emotional affect.Physical events typically relate to events wherein a discrete objectwithin the visual stimulus is moving, shifting locations, changingstates, etc. Any other portion of the visual stimulus that is notcategorized as an affective or physical event is categorized asnonaffective/nonphysical events. As will be understood by one ofordinary skill in the art, the use of the terms affective, physical,nonaffective and nonphysical events are merely used in the exemplarydiscussion below and are not intended to limit the spirit or scope ofthe present disclosure.

For the experiment(s) described herein, the following methodologies,testing equipment, parameters, and standards were followed: (1)determination of ratings of affective and physical events, (2)determination of instantaneous blink rate, (3) various permutationtesting in connection with assessing instantaneous blink rate, and (4)determination of control blink inhibition data. Further, various otherexperimental methodologies utilized in the present disclosure aresimilar to those used in prior patents and published papers by some ofthe same inventors of the present application, and are described in atleast the following patents that are hereby incorporated by reference;in particular: U.S. Pat. No. 7,922,670, titled System and Method forQuantifying and Mapping Visual Salience, issued Apr. 12, 2011, U.S. Pat.No. 8,343,067, titled System and Method for Quantifying and MappingVisual Salience, issued Jan. 1, 2013, and U.S. Pat. No. 8,551,015,titled System and Method for Evaluating and Diagnosing Patients Based onOcular Response, issued Oct. 8, 2013.

The aforementioned references generally relate to systems and methodsfor mapping and analyzing visual salience (the measure of a givenviewer's visual attention as it relates to or stands out againstanother's visual attention) as a viewer or group of viewers visuallyengage with a visual stimulus. In particular, the references generallydescribe various methods for recording, analyzing, and displaying visualsalience for an individual or a distributed group of individuals orproviding a mechanism to compare an individual's or selected group ofindividual visual responses to a known set of visual responses. Incertain embodiments, a monitoring device (or eye tracker) is generallyused in conjunction with the visual stimulus to measure the physicallocation at which a person is looking. Further, the earlier disclosuresdescribe methods for coordinating visual salience data to a specificinstance in time of the visual stimulus. This provides diagnosticinformation according to what a control or typical individual's datasuggest in comparison with a test individual, group of test individuals,or known data.

Still referring to the earlier patents incorporated herein by reference,in one embodiment, each viewer's set of data may be graphed on an x, y,and z-axis coordinate system, wherein the x and y dimensions generallyrepresent an area on the visual stimulus or point of regard with which aviewer engages (e.g., spatial fixation locations towards which aviewer's gaze is directed). Further, the z-axis generally corresponds totime and may be time-locked (or time-correlated or synchronized) withthe visual stimulus. Accordingly, multiple sets of data or lines can bemapped onto the same plot to generate a set of data or scan path,whether it is a control group (the group whose data is known and used asthe standard) or the group/individual being tested. Generally, at leasttwo trends emerge when analyzing the visual salience data: (1) the testdata (points) are loosely distributed and form a large radius (circle)if the points were connected in a circular manner, and (2) the test dataor points are closely clustered together forming a tight grouping and asmall radius (circle) if the points are connected in a circular manner.In the first instance, wherein the large circle is formed, this isgenerally called a divergent set meaning the data points are random andtend to spread away from each other. The second instance, wherein thesmall circle is formed, is typically called a convergent set meaning thedata points have a tendency to plot closely and gravitate towards eachother.

Further, the earlier patents referenced above further describe divergentdata sets as a particular instance in time or a particular frame wherethe majority of individuals engage different areas on the screen.Conversely and according to another embodiment, a convergent setdescribes a scenario where most individuals engage the visual stimulusin one area (x, y-axis) on the screen during a particular frame orinstance in time. Further, the individual data sets or lines areconnected radially and linearly to form a varying three-dimensionalshape that resembles a collection of cones or beehives horizontallyconnected together (also referred to herein as an attentional funnel).The wider parts of the cone represent divergent data sets and the closersegments of the cone represent convergent data sets. Thesethree-dimensional collections of data points are used to analyze andcompare various test individuals or test groups. In the scenario inwhich a test individual does not have visual salience data points inconvergent sets, the data point is flagged and noted. If there is apattern of data points existing outside of the convergent sets, there isan increased probability the testing individual is not engaging with thestimulus according to the control data. Further details of theaforementioned references will be described in greater detail inconnection with FIGS. 1B-1D.

Now referring to the specific experiment(s) and test data describedherein, test methodologies comprised the utilization of ninety-threechildren with a mean (M) chronological age of 2.3 years (SD=0.55)participating in the experiment(s) disclosed herein. The visual stimuluscomprised a video the children watched that included an unscriptedinteraction between a boy and a girl playing together in a toy wagon (asrepresentatively shown in FIG. 3). None of the participants hadpreviously viewed the video. Further, in unscripted scenes of naturalinteraction, the video included various physical and affective events.For example, a physical event shown in the video comprised a door of thewagon opening and closing. Similarly, an affective event shown in thevideo comprised an argument between the boy and the girl. Although, thephysical and affective events were not mutually exclusive, the locationsof the greatest affect were spatially discrete from those of mostmovement, with affectively charged facial expressions separated from thephysical location of the door.

The distinction between affective and physical events was relevant tothe experimental design because the children who watched the video weredivided into two groups that were expected to vary in their response toaffective and physical cues. The video was shown to 41 two-year-oldswith autism spectrum disorders (ASD) as well as 52 typicaltwo-year-olds. Here, the children with ASD provide a preferredcomparison group because the children have been shown previously todisplay atypical patterns of visual attention to social interaction,attenuated reactivity to varying social affect, and lack of differentialresponse to social attentional cues, but also intact response tophysical attentional cues and intact ability to predict and attend tophysical events. In the present experimental paradigm, blink inhibitionwas tested as a marker of perceived stimulus salience, varying by groupmembership.

FIG. 1A illustrates an exemplary blink and/or eye monitoring system 100for quantifying and mapping visual salience and for quantifying visualengagement over time as utilized in one test methodology. The system 100shown in FIG. 1 is a representation of the system used to test theindividuals in the experiment(s) described herein. The system includesat least one processor 102. Processor 102 may be any type of devicedesigned to receive and execute software programs, or that which isdesigned to be modified in functionality by software programs. Forexample, the processor 102 may be selected from a group comprisingdigital signal processors, microcontrollers, and microprocessors, or agroup consisting of field programmable gate arrays, and computerprogrammable logic devices. The functionality associated with theprocessor 102 may be centralized or distributed, whether locally orremotely.

In one embodiment, the processor 102 includes software executing thereonfor receiving data indicative of a group of individual's blink responsesto a visual stimulus 120. For example, the processor 102 may receive eyedata 112 from any number of eye trackers 110 or eye tracking devices.Each eye tracker 110 may be any device for tracking the blink responseof at least one eye of an individual (e.g., individual human or anyother species/animal). In one embodiment, the eye tracker 110 may be aninfrared video-oculography eye-tracking device. In another embodiment,the eye tracker 110 is a binocular eye tracker. In another embodiment,the eye tracker may comprise a blink monitoring system for identifyingblinks performed by a subject. In such an embodiment, the processor willreceive blink data 112 indicative of the subject's blink responses to avisual stimulus 120. In yet another embodiment, the eye tracker 110 maycomprise a combination of an eye-tracking device and a blink monitoringdevice, wherein the eye tracker 110 is capable of detecting eye data andblink data. According to another aspect, each eye tracker 110 maygenerate eye data 112 indicative of eye movement responses such as eyemovements, direction, dilation, rotation, gaze, blinking, etc. As willbe understood by one of ordinary skill in the art, eye data may includeblink data for exemplary purposes and is not intended to limit thespirit or scope of the present disclosure.

In one aspect based on the eye blink data 112, the processor maydetermine and/or identify a measurement of the timing and/orpresence/number of eye-blinks, how blinks are modulated between as wellas within tasks, and how the timing of blinks varies as a function ofviewer engagement and various stimulus events. Additionally theprocessor may determine blink rate before, during, and after a task,wherein the blink rate is measured and analyzed for variations at aplurality of intervals throughout the viewing of a visual stimulus; inparticular, before, during, and after viewing the visual stimulus. Blinkdata may also comprise measurements of instantaneous blink rate as itrelates to intratask blink inhibition.

In another aspect, based on the eye/blink data 112, the processor 102may determine and/or identify points of regard or fixation points. Apoint of regard (or point-of-gaze or fixation location) is a point atwhich an eye and/or both eyes of an individual are focusing. A point ofregard may be indicated as a coordinate in space (e.g., x, y, z) or atwo-dimensional coordinate data (e.g., x, y) on a surface or visualstimulus portrayed on a surface. A point of regard may additionally bereferenced with a time (t). Each point of regard may indicate a point offixation or any point of at which an eye is focusing regardless of thelength of time or fixation on the point.

In some embodiments, the system includes a visual stimulus 120. Thevisual stimulus 120 may be any visual stimulus such as a still image(e.g., print ad, webpage, painting, etc.), video imagery, a 2-D image orvideo, a 3-D image or video, a live video, a pre-recorded video,interactive media, etc. In an exemplary embodiment, the visual stimulus120 is a dynamic visual stimulus such as a video. The video may includeany imagery, broadcast, recording and/or representation of visual imagesof stationary or moving objects including, but not limited to, a motionpicture, a video game, and/or a recording of a live event. The video maybe embodied in any form of media such as film, video tape, DVD, CD-ROMand/or digital storage (e.g., storage 130). The visual stimulus 120 mayalso be a live event (e.g., theatrical performance, social interaction,training exercise, etc.) or any representation thereof (either two- orthree-dimensional).

Further, in other embodiments, the stimulus may comprise an audiostimulus (not shown), wherein an audio stimulus may comprise a liverecording, mp3, a compact disc, a DVD soundtrack, DVD audio without thepicture, or any other mechanism of the like. Accordingly, the eyetrackers 110 will monitor and record eye data 112 as a tester engageswith the auditory stimulus. In one aspect, the eye data 112 comprisesvarious eye movement responses for determining various areas offixation, the level of tester engagement with the stimulus, and variousdata in connection to blinking and blink inhibition. In other aspects,the eye data 112 indicative of various eye movement responses forassessing a stimulus's ability to engage specific viewers or groups ofviewers, to identify the most engaging special and temporal features ofa stimulus, and to categorize or index viewers as a function of theirengagement with a given stimulus, for either demographic or diagnosticpurposes.

Some embodiments of the system further include software utilized by theprocessor 102 for receiving stimulus data 122 from the visual stimulus120 or auditory stimulus (not shown). The stimulus data 122 may be, forexample, data representing the visual stimulus 120 (e.g., representationor video recording of a live event), a complete video visual stimulus120, or any portion of a visual stimulus 120 (e.g., frames and/orscreenshots). Similarly, the stimulus data 122 may comprise datarepresenting an audio stimulus (e.g., recording of audio, digitalrecording), a portion of an audio stimulus, etc. In some aspects, datamay also include time related info to enable mapping or time-lock of thestimulus to a plurality of eye and/or blink data.

The system may also include a database 130. The database 130 may becollocated with the processor 102 or may be remotely located andaccessible via a communications network. The database 130 may providetemporary storage for the processor 102 (e.g., random access memory)and/or permanent or semi-permanent data storage, e.g., for eye data 112or stimulus data 122. The system may further include any number ofdisplays 140. The display 140 may also be located either local or remoteto the processor 102. For example, the display 140 may be remotelylocated and receive data or information from the processor 102 via theInternet. As will be described below, data representing blink responses,blink assessments, points of regard, distributions of visual resources,and/or a group's distribution of visual resources and/or engagement tothe visual stimulus 120 may be presented on the display 140.

FIG. 1B shows an exemplary table 150 of data indicative of ocularresponses to a visual stimulus, or eye data 112. It should be understoodthat the eye data 112 may be organized and/or maintained in any manneror format and that the table is only exemplary. As such, the table 150may be organized in any manner such as in columns 154, 156, 159 asshown. In the present illustration, the data is referenced ascoordinates describing points of regard (or fixation locations). Forexample, an x-value of 300 is shown at 152 with a corresponding y-valueof 111 at 158. The coordinate in the present example further includes atime value in column 159, e.g., referring to a time that the particularcoordinate of eye data 112 was sampled. The time value may furthercorrespond to a time 164 of a visual stimulus 160. Any number ofadditional categories (e.g., columns) of eye data 112 may be representedsuch as a z-value referring to a distance for the point of regard.

As shown in FIG. 1B, a point of regard in the table 150 may be mapped tothe visual stimulus 160. For example, the point of regard referenced at152 and 158 may be mapped to a point 168 on a portion of the visualstimulus, e.g., using a coordinate system 162. In some embodiments, thecoordinate system 162 may relate to any video pixel coordinate system(e.g., 640×480 or 720×480). The portion of the visual stimulus 160 maybe a portion (e.g., frame or panel) corresponding to the time at whichthe point of regard was sampled.

The eye data 112 may include data sampled at any rate or frequency. Forexample, eye data 112 may be sampled from an individual at a samplingfrequency of 60 Hz, 512 Hz, 1000 Hz, or any other sampling frequency.The rate of visualization or presentation of eye data may be increasedor decreased as desired and/or adjusted based on a rate of change of thedynamic visual stimulus, e.g., 160. Both rates of analysis and rates ofpresentation of eye data may also be based on analysis of meaningfulsegments of video isolated for scrutiny. For example, if meaningfulevents in the stimuli occur at a rate of 30 times per second, rates ofsampling, analysis, and presentation could equal or exceed 30 Hz.

FIG. 1C shows a display 170 of several portions and/or frames of adynamic visual stimulus. As shown, the display 170 includes a time axis182 and any number of frames, e.g., 172, 174, corresponding to differenttimes of the dynamic visual stimulus. Further represented in the display170 are points of regard, e.g., 176, 178, 180, on the frame 172. Each ofthe points of regard may be determined from eye data 112 sampled fromdifferent individuals. Alternatively, each point of regard may bedetermined from different viewings of the same dynamic visual stimulusby one individual.

Referring to FIG. 1D, the system 100 according to the present disclosurefurther includes software for generating a display of the test group'sdistribution of visual resources to the visual stimulus. FIGS. 1Da-1Dhshow an example of a mechanisms by which to generate a display of thegroup's distribution of visual resources according to the presentdisclosure. FIG. 1Da shows two-dimensional representations of a group ofindividuals' distribution of visual resources (e.g., 184) at particulartimes in response to a visual stimulus. In FIG. 1Db, the distributionsare displayed topographically (e.g., 186) over the same period of time.As will be apparent to one skilled in the art upon reading the presentdescription, the group's distribution of visual resources is changingover the exemplary period of time (i.e., from left to right) fromdivergent to convergent (e.g., identifying an area of heightenedattention). FIG. 1Dc shows the group's distribution of visual resourcesat each time and a plane (e.g., 190) at an average (e.g., mean ormedian) value of relative salience or height value.

FIG. 1Dd shows each plane (e.g. 190) and an area of maximal salience(e.g., 192) provided by the plane at each time. FIGS. 1De and 1Dffurther show the areas of maximal salience (e.g., 192) at any number oftimes. To generate a preferred display of the group's distribution ofvisual resources according to the present disclosure, the areas may beconnected and/or extruded to develop an attentional funnel 195 over theperiod of time as shown in FIG. 1Dg. The funnel 195 may be mapped to thevisual stimulus and portions (e.g., frame 197) of the visual stimulusincluded in the display to show the areas of the visual stimulus whichcorrespond to the areas of maximal salience. As shown, a convergence isshown at the frame 197 indicating an area of heightened attention to theeyes of the male actor. FIG. 1Hd shows the frame 197 as well as twopreceding frames leading up to the convergence.

Turning now to a description of the collection and assessment of blinkdata in connection with the experiment(s) described in the presentdisclosure, FIG. 2 illustrates an exemplary data collection andassessment process 200 as disclosed in the experiment(s) herein. Variousaspects of the exemplary data collection and assessment process wereutilized a plurality of times with the various test participantsdescribed herein. In one embodiment, the first parameter examinedutilizing the process illustrated in FIG. 2 and commencing the presentexperiment(s) was overall blink rate and blink duration to test forphysiological differences in eye-blink behavior between toddlers withASD and typical toddlers. Eye movement data was recorded at the rate of60 Hz, and blinks were recorded as events with a measurable duration,identified by an automated algorithm, supplemented and verified bysimultaneous video recording in all participants (as described in step204 of FIG. 2), and separately verified by simultaneous electromyographyrecordings in one adult viewer.

At the beginning of each test session, participants viewed a children'svideo (e.g., Baby Mozart, Elmo) played on a computer monitor (step 202of FIG. 2). The computer monitor was mounted within a wall panel, andthe audio soundtrack was played through a set of concealed speakers.Toddlers were seated and buckled into a car seat mounted on a pneumaticlift so that viewing height (line-of-sight) was standardized for allchildren. Viewers' eyes were 30 in (76.2 cm) from the computer monitor,which subtended approximately a 23°×30° portion of each child's visualfield. Lights in the room were dimmed so that only images displayed onthe computer monitor could be easily seen. A five-point calibrationscheme was used, presenting spinning and/or flashing points of light aswell as cartoon animations, ranging in size from 0.5° to 1.5° of visualangle, all with accompanying sounds. The calibration routine wasfollowed by verification of calibration in which more animations werepresented at five on-screen locations. Throughout the remainder of thetesting session, animated targets (as used in the calibration process)were shown between experimental videos to measure drift in data. In thisway, accuracy of the eye-tracking and eye blink data 112 was verifiedbefore beginning experimental trials and was then repeatedly checkedbetween video segments as the testing continued. In the case that driftexceeded 3°, data collection was stopped and the child was recalibratedbefore further videos were presented. All aspects of the experimentalprotocol were performed by personnel blinded to the diagnostic status ofthe children. Most aspects of data acquisition and all aspects ofcoding, processing, and data summary are automated, such that separationbetween the diagnostic characterization protocol and the experimentalprotocol was assured.

To analyze blink inhibition as an index of perceived stimulus salience,children were shown a video scene of a boy and girl playing together ina toy wagon (some frames of which are shown FIG. 3). The video scene wasexcerpted from Karen Bruso and Mary Richardson's commercially availablechildren's video, Toddler Takes! Take 1: Toddlers at Play. The video waspresented in full-screen mode with an accompanying audio soundtrack on a20-in (50.8 cm) computer monitor 140 (refresh rate of 60 Hznon-interlaced), according to step 202 in FIG. 2. Video frames wereeight-bit color images, 640×480 pixels in resolution. The video framerate of presentation was 30 frames per second. The audio soundtrack wasa single (mono) channel sampled at 44.1 kHz. The original audiosoundtrack contained an instance of adult narrator voiceover; this wasremoved digitally to make the video scene as naturalistic as possible.The duration of the video was 1 min and 13.6 s. Individual measures ofblink rate and blink duration (see FIGS. 3 and 4) were measured duringvideo watching, as opposed to during intertrial intervals.

Before and after the video, a centering cue was presented on anotherwise blank screen to draw the attention of viewers to commonfixation location. The centering cue was 1.5° in visual angle withalternating blue and white sections, rotating in time to a chimingsound. During presentation of the centering cue, 91.4% of the childrenwere compliant in looking at the cue; there were no between-groupdifferences in the proportion of children who were compliant (z=1.12,P=0.24).

Visual fixation patterns were measured with eye-tracking equipment 110using hardware and software created by ISCAN, Inc. (see step 204 of FIG.2). The eye-tracking technology was video-based, using a darkpupil/corneal reflection technique with eye movement data collected atthe rate of 60 Hz. Analysis of eye movements and coding of fixation datawere performed with proprietary software written in MATLAB (MathWorks).The first phase of analysis was an automated identification ofnonfixation data, comprising blinks, saccades, and fixations directedaway from the stimuli presentation screen (see step 204 of FIG. 2). Thiseye tracking technology is exemplary only, and is not intended to limitthe spirit or the scope of the present disclosure.

Blinks were identified by an automated algorithm measuring occlusion ofthe pupil by rate of change in pupil diameter and by verticaldisplacement of the measured pupil center. As will be understood andappreciated, other methods could be used to detect blinks, such aseyelid closure, certain eyelid movement, and the like. The blinkdetection algorithm was supplemented by simultaneous video re-cording inall participants and verified by manual coding of the video data in 10%of participants' data. The algorithm was also verified by simultaneousvideo and electromyography (EMG) recording in one adult viewer. Incomparison with video recordings, the algorithm accurately detected95.0% of all blinks identified by manual coding of video images. Incomparison with EMG recordings, the algorithm accurately detected 96.4%of blinks recorded by EMG. Events identified by the algorithm as blinksbut shorter than 166.7 ms or longer than 566.7 ms were excluded fromanalysis in accordance with previous studies of blink duration and inagreement with visual inspection of the video images (blinks in FIG. 7,which appear longer than 566.7 ms, are actually multiple blinksseparated by brief fixations, obscured by the plot resolution). Durationmeasurements comparing blinks detected by the algorithm and blinksdetected by EMG were different by less than 10 ms (i.e., less than thesampling detection threshold of the eye-tracker). Saccades wereidentified by eye velocity using a velocity threshold of 30° per second.Off-screen fixations, when a participant looked away from the videoscreen, were identified by fixation coordinates to locations beyond thescreen bounds. Throughout all viewing data, the proportion ofnonfixation data (saccades+blinks+off-screen fixations) was notsignificantly different between the ASD (M=24.25%, SE=1.2) and typical(M=24.7%, SE=1.5) groups [t₍₉₁₎=0.22, P=0.82] (see step 204 of FIG. 2).

No difference was found in blinks per minute (bpm) between toddlers withASD (M=5.58 bpm, SD=3.88) and typical toddlers (M=5.18 bpm, SD=3.66)[t₍₉₁₎=0.519, P=0.60](FIG. 4). In addition, no difference in blinkduration was found between toddlers with ASD (M=300.0 ms, SD=98.7) andtypical toddlers (M=301.3 ms, SD=98.0) [t₍₉₁₎=−0.23, P=0.82]. Consistentwith previous research on the ontogeny of blinking, individual blinkrates (bpm) were positively correlated with chronological age in bothgroups (r=0.33, p<0.05 for the toddlers with ASD and r=0.27, P<0.05 fortypical toddlers.) There was no between-group difference in the strengthor direction of this correlation (z=0.28, P>0.05).

Anecdotal observation of variation in blink rate during the intertrialintervals before and after each experimental trial (the video scene)(see FIG. 6A) was also tested. During these intervals, a centering cuewas presented on an otherwise blank screen to draw the attention ofviewers to a common fixation location. Based on earlier observations, bythe indicators it was predicted that blink rate would decrease duringthe experimental trial relative to intertrial intervals.

As shown in FIG. 6B, the mean blink rate of both toddlers with ASD andtypical toddlers decreased during the experimental trial relative topre- and post-trial periods. Given the positive skew of the dependentvariable (bpm), with larger variance than mean, repeated measures ofanalysis of variance (ANOVA) [diagnostic group (2 levels)×trial type (3levels: pretrial, during trial, and post-trial)] with underlyingnegative binomial distributions assumed was performed. The ANOVA yieldeda significant main effect of trial type (Wald X²=18.70, df=2, P<0.001).Post hoc comparisons indicated that mean bpm pre- and post-trial werenot significantly different from one another (Wald X²=0.64, df=1,P=0.42), but that blink rate during each of those conditions wassignificantly greater than blink rate during the experimental trial(Wald X²=20.58, df=1, P<0.001 and Wald X²=14.57, df=1, P<0.001,respectively). There was no main effect of diagnosis (Wald X²=0.002,df=1, P=0.97) and no significant interaction of diagnosis by condition(Wald X²=0.003, df=2, P=0.99).

A determination of instantaneous blink rate as it relates to the blinkdata was also tested. Instantaneous blink rate is computed as a densityfunction. Data for each individual was recorded as 60-Hz time series.Binary values indicating whether a given individual was blinking or notwere recorded at each point in the time series (0 for not blinking and 1for blinking, with a contiguous sequence of 1's indicating a completeblink with duration equal to the length of that contiguous sequence) asdescribed in 206 of FIG. 2. At each time, t, in the time series,instantaneous blink rate was calculated according to the followingequation:

${{bpm}_{i}(t)} = {\frac{1}{\Delta\; t} \times \frac{n_{b}(t)}{N_{v}(t)}}$where bpm(t) is the instantaneous blink rate (blinks per minute) at timet, Δt is the sampling interval ( 1/60 s for 60-Hz sampling, converted tominutes as 1/3,600 min), n_(b)(t) is the sum of blinks (i.e., summedacross individuals) occurring at time t, and N_(v)(t) is the totalnumber of viewers either blinking or looking at the screen at time t.Finally, the instantaneous blink rate density function was smoothed witha Gaussian window (300 ms at full-width half-maximum) selected to matchthe mean individual blink duration.

Note that in a free-viewing experiment(s), N_(v)(t) should exclude anyparticipant looking away from the screen at time t. Also, note thatn_(b) is a fractional count of total blinks: a single blink lasting 300ms, measured in 60-Hz samples, would span 18 samples in the time seriesand would be counted as 1/18 of a blink at each time t.

Further, to test whether instantaneous blink rate was significantlymodulated during the video watching, permutation testing was used. Ineach of 1,000 iterations, the binary times series blink data for eachchild (0=not blinking, 1=blinking) were permuted by circular shifting,following the equation:b _(j,c)(t)=b _(j)(t−s _(j),moduloT)written asb _(j,c)(t)=b _(j)(t−s)_(T)),which, for s_(j)≥0, equals

${b_{j,c}(t)} = \{ \begin{matrix}{{b_{j}\lbrack {t - s_{j}} \rbrack},} & {s_{j} < 1 \leq T} \\{{b_{j}\lbrack {T - s_{j} + t} \rbrack},} & {0 \leq 1 \leq s_{j}}\end{matrix} $where b_(j) is the measured blink time series data for each participant,j; b_(j,c) is the circular-shifted blink time series data for the sameparticipant j; t is a time point in the time series defined over theinterval 0≤t≤T; T is the total duration of the stimulus (in the presentcase, the duration of the entire movie shown to participants); and s_(j)is the size of the circular shift, in the same units of time as t, foreach participant j. The size of the circular shift for each participantwas drawn independently from a random number generator with uniformdistribution, with possible values ranging from −T to T. After circularshifting, for each iteration, i, instantaneous blink rate was calculatedas previously described:

${{bpm}_{i}(t)} = {\frac{1}{\Delta\; t} \times \frac{n_{b_{c}}(t)}{{N_{v}}_{c}(t)}}$In this way, in each iteration, durations of blinks and interblinkintervals were preserved for each individual but the timing of eachblink was made random in relation to both the actual time line of videocontent and in relation to the timing of other participants' blinking.By this approach, in the permuted data, the mean blink rate ofparticipants during the entire task remains unchanged (andtask-specific), but the timing of when instantaneous blink rate isincreased or decreased is made random.

The permutation process was repeated on 1,000 iterations and thenmeasured against the statistical distribution of blink rate across alliterations at each point in the time series. At each time point acrossall iterations, the fifth percentile of permuted data was used as anonparametric threshold for identifying time points of significant blinkinhibition. This enabled the comparison of actual patterns of eyeblinking to randomized, chance patterns of eye blinking, enabling thenull hypothesis that the timing of eye blinks was unrelated to scenecontent to be tested.

Based on the experiment(s) above, it was found that the blink rate fortypical toddlers was significantly inhibited exhibiting values less thanthe 0.05 threshold of shuffled data) during 8.8% of video viewing timeand that the blink rate for the ASD group was significantly inhibitedduring 7.0% of viewing time. The difference between observed blink ratesand permuted data for each group was tested by two-sampleKolmogorov-Smirnov tests, finding significant differences for each(D=0.22, P<0.001 for typical toddlers and D=0.28, P<0.001 for toddlerswith ASD).

FIG. 9 shows graphs of the empirical cumulative distribution functionscomparing actual blink data with permuted data. These plots show both anincrease in low blink rates (the gap between actual data and permuteddata at the left end of abscissa) as well as an increase in blink rates(gap between actual data and permuted data at the right end ofabscissa).

It was tested whether instantaneous blink rate was significantlymodulated during the video itself (see FIG. 7A). Individual data wererecorded as 60-Hz time series (with binary values at each point in theseries indicating whether a given individual was blinking or not).Instantaneous blink rate was computed across all individuals for eachgroup. To test the null hypothesis that the timing of blink inhibitionwas unrelated to scene content, permutation testing was used. In each of1,000 iterations, for each group, the binary times series blink data foreach child were permuted by circular shifting, with shift size for eachchild drawn independently from a random number generator with uniformdistribution. Instantaneous blink rate was then calculated across theshifted individual data. Because each individual's data had been shiftedindependently, the timing of each shifted blink time series was randomin relation to the actual time line of video content and random inrelation to the timing of other participants' blinking. By thisapproach, in the permuted data, the mean blink rate of participantsduring the entire task remains unchanged (and task-specific), but thetiming of when instantaneous blink rate is increased or decreased ismade random.

This enabled a basic permutation test with exact probabilities: at eachtime point, the fifth percentile across all permuted data served as astatistical threshold (P=0.05) for identifying periods of statisticallysignificant blink inhibition (see FIGS. 7C and 7D). If the timing ofactual measured blinks was random with respect to ongoing video content,it was expected that the measured instantaneous blink rate for eachgroup would differ from that of the permuted data no more than 5% of thetime. In contrast, in the actual data, it was found that the blink ratefor typical toddlers was significantly inhibited (exhibiting values lessthan the 0.05 threshold of permuted data) during 8.8% of video viewingtime and that the blink rate for the ASD group was significantlyinhibited during 7.0% of video viewing time. This difference was testedbetween observed blink rates and permuted data for each group bytwo-sample Kolmogorov-Smirnov tests, finding significant differences foreach (D=0.22, P<0.001 for typical toddlers and D=0.28, P<0.001 fortoddlers with ASD).

As part of the correlation of blink inhibition to the visual stimulus120, a plurality of segments of the visual stimulus was identified asaffective content and physical content (also referred to herein asaffective events and physical events). Ten adults rated the affectivecontent of the video scene in a two-stage process. First, the entirevideo was divided into 15 segments, and viewers were asked to rank thesegments from most affective to least affective. Interrater coefficientof concordance for these rankings was highly significant (Kendall'sW=0.879, X²=123.02, df=14, P<0.0001). The eight segments ranked mosthighly were then used to identify precise timing of the affectiveevents. To do so, adult raters examined each of the eight most affectivesegments frame-by-frame and selected the time point at which theaffective event began and the time point at which the affective eventended. The SE of start and end times across all raters was 152 ms. Startand end times for each affective segment were averaged across the 10raters, resulting in eight affective events. Physical events weredefined as all-time points in which a wagon door was moving (with startand end points set by the start and stop of the door's motion). As willbe generally understood by one of ordinary skill in the art, events donot necessarily have to be categorized as affective or physical events,and such categorizations are merely used as exemplary purposes for thepresent experiment(s) and disclosure. Furthermore, a plurality ofmechanisms may be utilized for determining and measuring changes inblink rate in relation to any kind of event.

Having confirmed that blinking was inhibited at levels greater thanexpected by chance and inhibited at specific times during unconstrainedviewing of natural scenes, it was tested whether blink inhibition variedselectively with respect to video content, visual fixation, and viewergroup. As described above, the experimental paradigm presented twocategories of content (affective and physical events) to two populationsof children known for differential attention to those categories(children with ASD and typical toddlers). In the video shown toparticipants, the boy in the video desires to leave the wagon door open,whereas the girl wants it to be closed; this scenario convenientlycreated varying levels of affective content (the discord between the boyand the girl) and a repeated physical action (the closing or opening ofthe wagon door).

To operationalize the designation of affective and physical events in avideo of unscripted natural interaction, 10 adult viewers rated thelevel of affect throughout the entire video, identifying eight segmentswithin the video in which facial expressions and/or vocalizations showedheightened emotional affect (e.g., time periods when the boy or the girlin the video became visibly angry). The coefficient of concordance forinterrater affective ranking was highly significant (Kendall's W=0.879,X²=1223.02, df=14, P<0.00001). Physical events were operationalized astimes when the wagon door was moving. The two event types were notmutually exclusive but, per the independent raters, overlapped less than25.18% of the time.

The remaining segments of the video were classified asnonaffective/nonphysical events. It was predicted that viewers wouldinhibit their blinking during moments perceived to be particularlyimportant to process and would increase their blinking during momentsperceived to be less important. To examine how the timing of blinkinhibition varied with respect to affective and physical events,peristimulus (or “perievent”) time histograms (PSTHs) were used. PSTHswere constructed by aligning segments of individual time series blinkdata to the onset of events and by then computing counts of anindividual's blinks occurring in 33.3 ms bins in a surrounding 2,000 mswindow (as shown in step 208 in FIG. 2). Bin counts were computed foreach participant across all events and then averaged across allparticipants to obtain group means.

To test whether the observed changes in blink rate differed from thoseexpected by chance, a second set of PSTHs from permuted blink data wascomputed. As before, individual blink sequences were permuted bycircular shifting of individual data 1,000 times. PSTHs were thencomputed on each of those permuted datasets. The mean instantaneousblink rate, during each bin, across all 1,000 PSTHs from permuted dataquantified the blink rate one would observe if blink rate were randomwith respect to onscreen events. If, on the other hand, blink rate weretime-locked to onscreen events and not random, one would expect to seesignificant deviations from the permuted data distribution. The 5th and95th percentiles of instantaneous blink rate across all PSTHs frompermuted data served as a P=0.05 confidence level against which tocompare blink rates in the actual data (one-tailed comparisons). To testfor between-group differences, confidence intervals (CIs) ofbootstrapped data for each group were computed, as noted in step 216 ofFIG. 2.

As shown in FIG. 8A and described in step 218 of FIG. 2, the PSTH fortypical toddlers reveals a 32.4% reduction in blink rate for affectiveevents, reaching its minimum 66 ms prior to the zero lag. This indicatesstatistically significant blink inhibition in typical toddlers (P<0.05),time-locked to the occurrence of events with high affective valence.Toddlers with ASD also show a reduction in blink rate (35.8%), but thatreduction is greatest 599 ms after the zero lag of affective events (seeFIG. 8G).

The between-group difference in timing is highly significant, becausethe CIs of bootstrapped lag data for each group are nonoverlapping (seeFIG. 8M, lag time for blink rate minimum in typical toddlers: CI₅=−230ms, CI₉₅=0 ms; lag time for blink rate minimum in toddlers with ASD:CI₅=33 ms, CI₉₅=700 ms). The observed difference in timing was notattributable to a more general delay in speed or frequency of eyemovements, because it was found that no between-group differences inlatency to shift gaze [typical toddler: M=1.09 s (SE=0.20), toddlerswith ASD: M=0.96 s (SE=0.28); t₍₉₁₎=0.40, P=0.69, measured as reactiontime to initiate a first saccade following the onset of the movie] or induration or frequency of fixations [duration for typical toddlers: M=442ms (SE=16.4), duration for toddlers with ASD: M=492 (SE=29.4);t₍₉₁₎=−1.57, P=0.12 and frequency for typical toddlers: M=2.04 fixationsper second (SE=0.09), frequency for toddlers with ASD: M=1.93 (SE=0.11);t₍₉₁₎=0.85, P=040].

Each group shows a numerical, although not statistically significant,reduction in blink rate by event type (see FIG. 8N): Typical toddlersexhibit greater reduction in blink rate during affective than physicalevents (32.4% vs. 25.4%, FIGS. 8A and 8B), whereas toddlers with ASDexhibit the reverse pattern, with a 41.7% reduction for physical eventsand a 35.8% reduction for affective events (see FIGS. 8G and 8H). Bothgroups of toddlers show a significant increase in blink rate relative tononaffective nonphysical events (see FIGS. 8C and 8I). Helping todisambiguate the question of differential engagement is the pattern ofeach group's visual fixations during the two event types (see FIG. 8O).Typical toddlers spent significantly less time looking at objects thantoddlers with ASD during both event types [F_(1,91)=12.01, P=0.001,repeated measures ANOVA with diagnosis (2 levels)×event (affective vs.physical)], and the interaction between diagnosis and event type wassignificant (see FIG. 8O) (F_(1,91)=5.99, P=0.016). Paired-samples ttests confirmed that typical toddlers showed no difference in percentageof fixation on objects during affective vs. physical events(t_(1,51)=0.85, P=0.4; M_(affective)=25.5%, SD=14.21 vs.M_(physical)=26.5%, SD=16.7), but that toddlers with ASD increasedfixation on objects, such as the moving wagon door, during physicalevents (see FIG. 8O) [M (SD)=33.9(16.7) for affective vs. 40.0(17.2) forphysical; t_(1,40)=3.57, P=0.001].

In sum, blink inhibition for typical toddlers was (i) most reduced justprior to the zero lag of events, (ii) numerically greater for affectiverather than physical events, and (iii) unrelated to level of fixation onobjects (marked instead by greater than 73% fixation on people duringboth event types). In contrast, for toddlers with ASD, blink inhibitionwas (i) most reduced after the zero lag of events, (ii) numericallygreater for physical rather than affective events, and (iii) marked by asignificant increase in fixation on objects during physical events (seestep 220 of FIG. 2).

Referring now to several of the figures, in one embodiment, FIG. 6Cillustrates an exemplary embodiment of task dependent modulation ofblinking between two different viewer groups (viewer group A and viewergroup B), in particular, an exemplary blink rate comparison between twodifferent viewer groups (see FIG. 6E) observing three different eventstimuli (see FIG. 6D). In one embodiment, viewer group A and viewergroup B do not exhibit blink inhibition while viewing event A. Accordingto one aspect and as illustrated in FIG. 6C, as both viewer groupsengage with event A, neither group inhibited their blink rate as bothgroups' bpm remained between approximately 9 and 13 blinks per minute.

According to one aspect illustrated in FIG. 6C, in connection with eventB, viewer group B's blink inhibition is modulated while the viewer groupengages with event B of the visual stimulus. Alternatively, viewer groupA's blink inhibition is not modulated and generally hovers around 9-13blinks per minute. In one aspect, this demonstrates that viewer groupB's level of engagement notably increases during engagement with eventB; whereas viewer group A's level of engagement generally does notchange.

Further, in another aspect, both viewer groups' levels of engagementremain consistent during the viewing of event A within the visualstimulus. Blink inhibition remains relatively close for both viewergroups around 9-13 blinks per minute. The discussion example of FIG. 6Cdemonstrates various levels of engagement amongst groups of people inresponse to certain events (e.g., events within a given stimulus, orbetween different stimuli entirely, etc.). For example, a marketing firmmay use such an assessment in determining a marketing campaign's powerto engage a target demographic. Accordingly, if a marketing campaign isintended to target a demographic of 30-40-year-old women, specificallyduring predetermined segments of a visual stimulus, viewer group A maycomprise 30-40-year-old men and viewer group B may comprise30-40-year-old women. If event B comprises the predetermined segmentdesigned to captivate individuals belonging to viewer group B, then inthis exemplary description the marketing campaign may be successful atincreasing the level of engagement (assessed by measuring blink rate andblink inhibition) for viewer group B during event B. As will begenerally understood, the aforementioned description is for exemplarypurposes and is not intended to limit the spirit or scope of the presentdisclosure.

FIG. 7 illustrates an exemplary representation of statisticallysignificant blink inhibition during natural viewing of a video scene,wherein data are plotted over time such that a time-lock with the visualstimulus occurs for further analyzation of the blink data, according toone embodiment of the present disclosure. In one embodiment, FIG. 7Aillustrates an exemplary raster plot depicting eye blinks made bytypical toddlers while watching an exemplary video scene. Similarly, inanother embodiment, FIG. 7B illustrates instantaneous blink ratetime-locked with the visual stimulus. According to one aspect, thehigher points in the curve of FIG. 7B represent where viewers exhibitedsignificant amounts of blinking (e.g., generally points closer to 17-20blinks per minute) whereas points in the curve that are lower representpoints in time where the viewer did not blink as often (e.g., generallypoints closer to 0-2 blinks per minute).

In another embodiment, FIG. 7C illustrates an exemplary plot of the5^(th) and 95^(th) percentiles of permutated data (mechanisms fordetermining permutated data were previously discussed in further detailherein) for typical toddlers. In one aspect, the 95th percentilerepresents increased blinking and the 5th percentile representsdecreased blinking. Plotted data shown in FIG. 7C that corresponds tothe 5th percentile of instantaneous blink rate are used to generate FIG.7D, wherein FIG. 7D illustrates an exemplary plot of instances of blinkinhibition mapped (synchronized) with particular times in the videoscene according to another embodiment of the present disclosure. Asshown in FIG. 7, corresponding times of blink inhibition 705, 710, 715,and 720 can be seen in each of the plots of FIGS. 7A-D. Similarly,various mechanisms associated with time-locking a visual stimulus toblink data, an auditory stimulus may be time-locked with blink data tofurther determine periods of blink inhibition as it relates to theauditory stimulus.

FIG. 8 illustrates exemplary data in connection with time-locked(synchronized) blinks and blink inhibition during natural viewing,together with example visual fixation data, according to one embodimentof the present disclosure. In some aspects, the present experiment(s)measured time-locking of blinks and blink inhibition relative toaffective events (see FIGS. 8A and 8G), physical events (see FIGS. 8Band 8H), and nonaffective/nonphysical events (see FIGS. 8C and 8I) byconstructing PSTHs. PSTHs show the percent change in bpm relative to themean of permuted blink data. Dashed horizontal lines mark 0.05 and 0.95CIs; the percent change in bpm beyond these levels represents a changein bpm greater than expected by chance (one-tailed, P<0.05). CIs scaleinversely with the number of events (with approximately double thenumber of events in the nonaffective nonphysical category).

According to further aspects, absolute minimum and maximum changes inbpm are highlighted by black squares in each plot. Exemplary visualfixation data during changing in blink rate for typical toddlers andtoddlers with ASD are illustrated relating to affective event in FIGS.8D and 8J, relating to physical events in FIGS. 8E and 8K, and relatingto nonaffective/nonphysical events in FIGS. 8F and 8L, respectively.Three column plots show a still frame from the video (first column,sampled at the absolute minimum decrease in bpm); kernel density plot offixation data at the same moment (second column, with hotter colorsdenoting greater density); and the same kernel density plot scaled fromblack to transparent, overlaid on the original frame (third column). Thecolor of fixation density plots is scaled relative to the sample size ofeach group, such that maximum and minimum possible densities have thesame color values for each group despite differences in sample size.FIG. 8M illustrates timing of blink inhibition for affective vs.physical events. FIG. 8N illustrates percent decrease in bpm foraffective vs. physical events. FIG. 8O illustrates percent fixation onobjects for affective vs. physical events.

FIG. 10 illustrates an overview 1000 of an exemplary process fordetermining the level of viewer engagement activity with respect to agiven stimulus. The exemplary determination of viewer engagementactivity process utilizes similar mechanisms as described in connectionwith FIG. 2. For example, the process generally initiates by displayinga visual stimulus to a viewer (see step 1002). As previously described,a visual stimulus may comprise a plurality of forms of media including:a DVD, stored digital media, a video game, etc. Subsequently, aprocessor 102 receives and records blink data (see step 1004)corresponding to a viewer while simultaneously receiving stimulus data122 that corresponds to the visual stimulus. Blink data is generallycaptured via an eye tracker or eye-monitoring device 110 beforepropagating to the processor. The processor typically comprises softwareenabling the blink data to be transformed to a usable, assessable formatas executed in process 1006.

Further, the processor 102 generally comprises software to time-lockstimulus data 122 to a usable and assessable format of blink data (seestep 1008). As previously discussed, time-locking blink data withstimulus data 122 enables assessment of blink behavior and blinkinhibition relative to various indicators within the visual stimulus. Insome instances, the process of determining viewer engagement activitymay involve gathering data for one or a plurality of viewers, whereingenerally for assessing a plurality of viewers the aforementioned steps1002-1010 are repeated until the desired group of viewers is attained.Furthermore, viewers may be categorized into various demographicsaccording to the intent and spirit of the target experiment(s) (step1012). For example, an illegal-substance screening stimulus may becreated, wherein a viewer is tested for their level of engagement as heor she views various illegal substances to potentially categorize as auser or ex-user of illegal substances.

Additionally at step 1014, the processor 102 generally comprisessoftware to aggregate time-locked or synchronized blink data, forexample, by combining and permuting the data for many viewers. In oneembodiment, the aggregated time-locked blink data is generally assessedfor a plurality of indicators including: determining an individual'sinstantaneous blink rate, the probability of whether an individualblinked or will blink, etc. Generally, these indicators are synchronizedwith one or multiple points in time with respect to the visual stimulus120. Utilizing the aggregated and parsed blink data, the results areassessed to identify various patterns of blink inhibition (see step1016) and compared with predetermined indicators within the stimulusdata and other predetermined factors (see step 1018). Subsequently, ageneral assessment can be made regarding the viewer(s) level ofengagement as it relates to various events within the visual stimulus(see step 1020). For example, when engaging a viewer regarding anillegal substance screening, a drug user may vary his or her level ofengagement or blink timing when shown an illegal substance versussomeone who does not use illegal substances.

In one embodiment, FIG. 10 may also describe a similar process fordetermining engagement activity as it relates to listening to anauditory stimulus, wherein step 1002 a listener hears/listens to anauditory stimulus. Accordingly, the remaining steps of the processdescribed in FIG. 10 would generally be similar to those utilized for avisual stimulus. Similarly, a listener's measure of blink inhibitiondetermines the listener's level of engagement to the auditory stimulus.For example, teachers may want students to listen to an auditorystimulus for teaching a foreign language. The teachers may utilize thisexemplary process to assess how engaged students are to the teachingaid.

According to one embodiment of the present disclosure, FIG. 11illustrates an overview of an exemplary process 1100 for categorizing orrating viewers as a function of their engagement with a given stimulusfor assessing disease/disorder state, wherein the assessment ofdisease/disorder state generally comprises the presence/absence of acondition, disease/disorder state progression, and/or treatment responsein connection with a prior diagnosis. Similar to some other processes inthe present disclosure, the process for assessing disease/disorder state1100 generally commences with displaying a visual stimulus (see step1102) to a viewer on a monitor device 140, receiving and recording atthe processor 102 blink data for the viewer (see step 1104), andconversion of blink data to assessable format (see step 1106) at theprocessor.

The processor 102 retrieves predetermined time-stamped event data (step1108) corresponding to the visual stimulus 120 from the database 130.The predetermined time-stamped event data may relate to a number ofparameters. For example, the data may comprise control data describingpatterns of time moments at which either typical or atypical viewersheighten or lessen their level of engagement according to various eventindicators. For example, the time stamped events could relate tophysical or affective events in the visual stimuli. The recorded viewerblink data is then time-locked to the visual stimulus and compared tothe predetermined time-stamped data (see step 1110) to identify variouspatterns of blink inhibition and further to determine levels ofengagement throughout the visual stimulus. Further in certainembodiments (described in greater detail below), in addition tocomparing the level of engagement at particular points in time, acomparison is made to locations of visual fixation according to varioustimes within the visual stimulus. The comparison can be used foridentifying areas of convergence and divergence within the data setssuch that an assessment may be generated to determine if the viewer'sblink data exists within/outside of the limits of the predeterminedranges of acceptable data (see step 1112). Subsequently, a furtherassessment can be made (see step 1114) as to the disease/disorder stateof the viewer utilizing the comparison of the control data and theviewer's blink inhibition data. For example, if blinking is inhibitedbefore a predetermined event, then a toddler may be categorized astypical, but if blinking is inhibited after a predetermined event, thena toddler may potentially show early signs of ASD.

As will be generally understood by one of ordinary skill in the art,typical toddlers and toddlers diagnosed with ASD were assessed duringthe experiment(s) described by aspects of the present disclosure, butany viewer group may be targeted and analyzed for various levels ofviewer engagement using the disclosed mechanisms of gathering andanalyzing eye data. For example, a viewer group may comprise a group ofteenagers for marketing research, a group of college students for apsychology experiment(s), a group of adults for medical testing, etc.

The process described in connection with FIG. 11 may also be utilized torank and/or categorize viewers depending on a viewer's level ofengagement. In one embodiment, based on the assessed level of engagementof a viewer, a further assessment may be made in connection withcategorizing the viewer. In one aspect, utilizing blink data it ispossible to determine a viewers' level of engagement with a stimulus,wherein the various levels of engagement may provide a viewer ranking ormaybe further used in conjunction with a predetermined index to classifyor categorize viewers. For example, a flight simulator may be developedto engage potential flight school candidates and generate reports thatassess and categorize the potential candidates according to a potentialsuccess rate for flight school.

According to one aspect similar to the process described in connectionwith FIG. 11, in connection with categorizing a viewer or group ofviewers as a function of their engagement with a given stimulus, steps1102-1112 can be repeated for gathering and comparing blink data.Accordingly, similar to step 1114, an assessment as to a viewer's indexlevel, rating, or category can be provided as a function of viewerengagement. For example, students in a school may be classified based ontheir level of engagement with a lecture, such that teachers would knowand understand which students need more attention based on theirengagement level.

In one embodiment, FIG. 11 may also describe a similar process forassessing/categorizing as it relates to listening to an auditorystimulus, wherein step 1102 a listener hears/listens to an auditorystimulus. Accordingly, the remaining steps of the process described inFIG. 11 would generally be similar to those utilized for a visualstimulus. Similarly, a listener's measure of blink inhibition determinesthe listener's level of engagement to the auditory stimulus. Forexample, an auditory stimulus may comprise a mechanism for assessing thedisease/disorder state of various individuals. As predetermined blinkpatterns may be known, if a listener does not follow or correlate withthe predetermined blink data, the individual may be categorized into acertain group as it relates to a state of a mentaldisease/disorder/condition. Further a mental disease/disorder/conditionmay comprise a cognitive or developmental disease/disorder/condition.

In one aspect similar to the process describe in connection with FIG.11, concerning analyzing the measure of a stimulus's ability to engage aviewer, the blink data may be compared to a predetermined index thatcorrelates viewer engagement to a stimulus's power to engage a viewer orgroup of viewers. In another aspect, the processor 102 may retrievepredetermined time event data and using an algorithm to determine astimulus's ability to engage viewers. For example, a marketing companymay have a predetermined viewer engagement index the dictates theprediction of success of a marketing campaign. During trial showings ofvarious marketing campaigns, if the marketing campaign does not reachthe minimum predetermined viewer engagement index by successfullyengaging viewers, it will not be released for marketing.

According to another embodiment, blink inhibition data may provide aquantifiable metric for the level of effectiveness a visual stimulus mayhave by utilizing a measure of perceived visual salience. In one aspect,effectiveness of a visual stimulus may comprise using an index of viewerengagement to determine whether the visual stimulus possesses thedesired or undesired effect of captivating a viewer. As previouslydescribed and according to one aspect, the level of viewer engagementmay be analyzed with blink data and time-locked with particular pointsin the visual stimulus (e.g., frame-by-frame, predetermined segments,etc.). Therefore, according to one aspect, determining the level ofengagement of a viewer at particular points will assist in determininghow engaging a visual stimulus is at that particular point or segment toan audience or viewer. Similarly, level of engagement of a viewer mayhelp to identify the effectiveness of a whole or complete visualstimulus at captivating an audience or individual. For example, amarketing company may utilize level of engagement trends via capturingmeasures of blink data during trial testing of marketing campaigns as anindication of the campaign's ability to engage. A marketing company mayalso utilize blink data to determine whether stimulus A is moreeffective than stimulus B to verify unproven/untested theories.

In one embodiment, blink inhibition data can be utilized a similarprocess for determining the power of an auditory stimulus's power toengage as it relates to a listener listening to an auditory stimulus,wherein the initial step a listener hears/listens to an auditorystimulus. Accordingly, the remaining steps of the aforementionedprocesses would generally be similar to those utilized for a visualstimulus. Similarly, a listener's measure of blink inhibition determinesthe listener's level of engagement to the auditory stimulus. Forexample, teachers may want students to listen to an auditory stimulusfor teaching a foreign language. The teachers may utilize this exemplaryprocess to assess how engaged students are to the teaching aid.

In one embodiment and as previously described, blink data correlatedwith viewer level of engagement that is used to categorize a viewer maybe further used in connection with determining effectiveness of a visualstimulus. For example, a particular visual stimulus may be targeted for10-14-year-old girls. By testing a sample of a target audience, blinkdata may further assist in identifying effectiveness of the visualstimulus intended for a target audience. Further, a comparison may bemade between 6-10-year-old girls and 10-14-year-old girls for example,to ensure the accuracy of viewer engagement.

FIG. 12 illustrates one embodiment of the present disclosure comprisingan exemplary process 1200 for collecting and assessing various types ofdata including assessing perceived stimulus salience of a visualstimulus. In one aspect, the eye tracker 110 may comprise a combinationof an eye tracker and a blink monitor, wherein the eye tracker recordseye movement data (e.g., saccades, fixations, pupil dilations, etc.) andthe blink monitor records blink data. In another aspect, the eyemonitoring system may comprise a separate eye tracker and blink monitorworking in conjunction to send eye and blink data 112 to the processor102 for assessment. According to one aspect of the present embodiment,eye data 112 received by the processor 102 (see step 1205) can beutilized to create attentional funnels (as previously described herein)for quantifying and mapping visual salience to further assess a viewer'slevel of engagement.

As previously described, the eye tracker may receive and record blinkdata and eye data similarly to other processes described in the presentdisclosure (see steps 1204 and 1205). Eye movement and blink data can beconverted to assessable formats (see step 1206), such as binary blinkdata and point of regard (point-of-gaze) coordinate data and theprocessor 102 can retrieve predetermined time stamped events for thevisual stimulus 120 (see step 1208). Similar to the process described inconnection with FIG. 2, eye and blink can be collected for many viewersin a group and the viewers can be optionally categorized based ondesired engagement demographics or other category criteria (see steps1210 and 1212).

Accordingly, the processor 102 can create a mapping of perceived visualsalience, compare time-locked eye data to predetermined data, andprovide an assessment of perceived stimulus salience. In one embodiment,a quantified mapping of perceived visual salience describes a viewer'sfixation or point of regard, wherein the point generally determines thelocation on a stimulus one focuses his or her attention. In one aspect,eye data 112 corresponding to a control group or control data can beused to create an attentional funnel (previously described in connectionwith FIG. 1D) delineating areas of convergence and divergence. Aspreviously described, areas of convergence generally representparticular instances in time where the majority of viewers distributionof point of regards are within a small two-dimension area on a stimulus.This data and/or display can be overlaid or time-locked to blink datafor the same stimulus, which enables confirming diagnostic methods, etc.This mechanism is utilized to assess viewers who do not follow thedistribution of convergence and further may be able to provide anassessment of disease/disorder state.

In one embodiment illustrated in FIG. 13, a process 1300 is illustratedfor identifying the most engaging temporal and spatial features within astimulus. Similarly to the process described in FIG. 12, a stimulus maybe displayed to one or more viewers (step 1302), eye-movement and blinkdata may be recorded (steps 1304 and 1305) and converted to anassessable format (step 1306), and the eye-movement and blink data canbe time synchronized to the visual stimulus (step 1308). Further, thetime-locked eye and blink data may be permuted to quantify theprobability of blink response during the viewing of the visual stimulusfor participating viewers (see step 1312). As previously described,permuting blink data enables an accurate analysis of blink response;hence, enabling periods of blink inhibition to be identified for a givenviewer or group of viewers (see step 1314).

Through the aforementioned process of quantifying visual salience, theeye-movement data is assessed to determine areas of fixation andcorrelated with periods of blink inhibition to identify the mostengaging temporal and spatial features of a visual stimulus (see steps1316 and 1318). In particular, if it is assumed a viewer is engaged witha video exhibiting blink inhibition, then identifying location offixation data may indicate spatial and temporal locations of the videothat are most engaging. Through a “data-mining” process executingnumerous trials to collect a plurality of data, assessing the level ofviewer engagement to a visual stimulus, and specifically to variousfeatures within the stimulus, can help assess the most engaging temporaland spatial features of the stimulus based on time-varying viewerengagement.

FIG. 14 illustrates an exemplary process 1400 of assessing patientcondition state, according to one embodiment of the present disclosure.Similarly to the process described in connection with FIG. 11, blinkdata is recorded (step 1404), converted to assessable format (step1406), and compared to time-stamped events to identify various blinkpatterns (step 1410). A further assessment regarding the level of blinkpatterns relative to predetermined ranges is conducted to identifyseverity, mildness, or change of condition state (see step 1412). Forexample, a viewer may be previously diagnosed with a cognitive conditionand is routinely assessed to monitor the state of that cognitivecondition. Further, the predetermined data that the newly acquired blinkdata is compared to may comprise previous data from the viewer at anearlier stage of his or her diagnosed condition. According to themeasure of condition severity, longitudinal data may be collected todetermine the measure of change in the state of the present condition(see step 1416).

Further Analysis

According to some embodiments of the present disclosure (in particular,the experiment(s)s described above), patterns of blink inhibition andthe distribution of visual fixations map onto well establishedbetween-group differences, but also reveal more subtle differences inthe subjective assessment of stimulus salience. For example, accordingto one aspect, when data were time-aligned to scenes of heightenedaffective content (FIG. 6A), typical toddlers showed a persistentinhibition of blinking that peaked before the zero event lag. Toddlerswith ASD, in contrast, exhibited a peak in blink inhibition thatoccurred more than 0.5 s after the zero event lag.

That typical toddlers inhibit their blinking earlier than toddlers withASD shows the unexpected possibility that typical toddlers are activelyanticipating the unfolding of salient events, and are doing so intime-locked fashion. The visual fixation data tell a similar story:Toddlers with ASD look more at physical objects in the video scene andselectively increase their fixation on those objects when the objectsmove (that is, during the designated physical events). Accordingly,utilizing time-locked blink inhibition and/or visual fixation data canbe used in assisting diagnosis of various cognitive disorders ordegradations.

In contrast, typical toddlers' attention to socially relevant cues, suchas eye-gaze, facial expression, and body posture, may allow them toanticipate actions that have not yet happened but may be about to happen(as when angry facial expressions precede a yell or the slamming of thewagon door). These cues help typical toddlers generate expectationsabout how actions in the world will subsequently unfold. For toddlerswith ASD, however, blink inhibition, as an after-the-fact reaction, canbe seen as reflecting a lack of sensitivity to those environmental (and,in particular, social) cues. It suggests an engagement with affectiveand physical stimuli separate from the social context in which they aretypically perceived: although typical toddlers may be engaged by theslamming of the car door because of its relevance to the ongoing socialinteraction between the characters, engagement by toddlers with ASD maybe in reaction to the salient physical properties of such events.

These hypotheses regarding between-group differences in how movie eventswere perceived underscore the point that even though movie events may beclassified as affective or physical, it is unlikely that they wereperceived as mutually exclusive dualities. One of the main goals of theexperiment(s) was to test for blink inhibition using semi-structured,naturalistic stimuli. In such situations, categorical boundaries ofaffective and physical become blurred: typical toddlers, for instance,are likely to perceive the social significance and affective meaningbehind the slamming wagon door. This blurring of affective and physicalcategories may account for why reductions in blink rate trended in theexpected directions but did not reach statistical significance in thisparticular analysis, with typical toddlers showing a larger reduction inresponse to affective events, whereas toddlers with ASD showed greaterreduction to physical events. Further, events do not necessarily have tobe categorized into either affective or physical events to determine alevel of engagement, as will be understood by one of ordinary skill inthe art.

The results demonstrate that patterns of blink inhibition can provide aninroad into an aspect of social affective experience that has beenlacking in the field of autism research and in many neuroethologicalstudies of visual perception in general: a measure of not only whatsomeone is looking at but of how engaged he or she is with what he orshe is looking at. Although previous work has shown that children withASD allocate fewer attentional resources to socially relevant stimulithan their typically developing peers, these studies have failed tocapture how engaged children are with what they are fixating on.

Further, measures of blink inhibition are well suited to providingtemporally precise indices of perceived stimulus salience duringnaturalistic, fast-paced presentations of visual content. In comparisonto other autonomic responses traditionally used in psychophysiologicalstudies, such as electrodermal and cardiovascular activity, blinkinhibition compares well for measuring reactivity to emotional stimuli:electrodermal and cardiovascular responses are highly multi-determined,preventing strong inferences about their relationship to mentalactivity; in addition, their latency and refractory periods undermineprecise temporal markings of their measurements relative to affective orcognitive state. Blink inhibition, in contrast, is intrinsic to thevisual system rather than a peripheral function; its on- and off-setparameters are precise and temporally sensitive to ecologically valid,fast-paced presentations of content; and, finally, blink inhibition canbe measured by entirely noninvasive, even concealed, eye-trackingcameras, or other devices, circumventing the need for obtrusiveequipment that would alter the ethological validity of other measures.

In one embodiment, the present systems and methods provide a mechanismto assess viewer behavior, features of stimuli, and the interactionbetween viewer behavior and stimuli. Specifically and according to oneaspect, because blinking interrupts the flow of visual information to aviewer, and because the inhibition of blinking ensures that the flow ofvisual information will not be disrupted, measurements of the precisetiming of when individuals do or do not inhibit their blinking canprovide robust quantitative indices of viewer engagement and thesubjective assessment of perceived stimulus salience, even thoughindividuals are largely unaware of their own blinking during everydaysituations. Therefore, the systems and methods described herein forquantifying blink response and blink inhibition providemoment-by-moments measurements of viewer engagement by measuring what isor is not engaging enough to warrant viewers' inhibition of blinking.

One embodiment of the present disclosure describes measures of visualscanning, eye movements, blink data, and blink timing data to derive ameasure of how engaged a person is with what he or she is looking at. Inone aspect, blink related data as a measure of viewer engagementprovides a mechanism for determining the most engaging spatial andtemporal aspects of a stimulus. According to another aspect, measures ofblink inhibition provide a promising index of autonomic reactivity anddifferential engagement, time-locked to salient moments withinfast-paced, rapidly changing visual displays. By precisely measuring thetiming of blink inhibition relative to unfolding content, one candetermine, on a moment-by-moment basis, a viewer's subjective assessmentof the importance of what he or she is watching.

Accordingly, it will be understood that various embodiments of thepresent system described herein are generally implemented as a specialpurpose or general-purpose computer including various computer hardwareas discussed in greater detail below. Embodiments within the scope ofthe present disclosure also include computer-readable media for carryingor having computer-executable instructions or data structures storedthereon. Such computer-readable media can be any available media whichcan be accessed by a general purpose or special purpose computer, ordownloadable through communication networks. By way of example, and notlimitation, such computer-readable media can comprise physical storagemedia such as RAM, ROM, flash memory, EEPROM, CD-ROM, DVD, or otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, any type of removable non-volatile memories such as securedigital (SD), flash memory, memory stick etc., or any other medium whichcan be used to carry or store computer program code in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer, or a mobiledevice.

When information is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a computer, the computer properly views theconnection as a computer-readable medium. Thus, any such a connection isproperly termed and considered a computer-readable medium. Combinationsof the above should also be included within the scope ofcomputer-readable media. Computer-executable instructions comprise, forexample, instructions and data which cause a general purpose computer,special purpose computer, or special purpose processing device such as amobile device processor to perform one specific function or a group offunctions.

Those skilled in the art will understand the features and aspects of asuitable computing environment in which aspects of the disclosure may beimplemented. Although not required, the inventions are described in thegeneral context of computer-executable instructions, such as programmodules or engines, as described earlier, being executed by computers innetworked environments. Such program modules are often reflected andillustrated by flow charts, sequence diagrams, exemplary screendisplays, and other techniques used by those skilled in the art tocommunicate how to make and use such computer program modules.Generally, program modules include routines, programs, objects,components, data structures, etc. that perform particular tasks orimplement particular abstract data types, within the computer.Computer-executable instructions, associated data structures, andprogram modules represent examples of the program code for executingsteps of the methods disclosed herein. The particular sequence of suchexecutable instructions or associated data structures represent examplesof corresponding acts for implementing the functions described in suchsteps.

Those skilled in the art will also appreciate that the invention may bepracticed in network computing environments with many types of computersystem configurations, including personal computers, hand-held devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, networked PCs, minicomputers, mainframe computers, and thelike. The invention is practiced in distributed computing environmentswhere tasks are performed by local and remote processing devices thatare linked (either by hardwired links, wireless links, or by acombination of hardwired or wireless links) through a communicationsnetwork. In a distributed computing environment, program modules may belocated in both local and remote memory storage devices.

An exemplary system for implementing the inventions, which is notillustrated, includes a general purpose computing device in the form ofa conventional computer, including a processing unit, a system memory,and a system bus that couples various system components including thesystem memory to the processing unit. The computer will typicallyinclude one or more magnetic hard disk drives (also called “data stores”or “data storage” or other names) for reading from and writing to. Thedrives and their associated computer-readable media provide nonvolatilestorage of computer-executable instructions, data structures, programmodules, and other data for the computer. Although the exemplaryenvironment described herein employs a magnetic hard disk, a removablemagnetic disk, removable optical disks, other types of computer readablemedia for storing data can be used, including magnetic cassettes, flashmemory cards, digital video disks (DVDs), Bernoulli cartridges, RAMs,ROMs, and the like.

Computer program code that implements most of the functionalitydescribed herein typically comprises one or more program modules may bestored on the hard disk or other storage medium. This program code, asis known to those skilled in the art, usually includes an operatingsystem, one or more application programs, other program modules, andprogram data. A user may enter commands and information into thecomputer through keyboard, pointing device, a script containing computerprogram code written in a scripting language or other input devices (notshown), such as a microphone, etc. These and other input devices areoften connected to the processing unit through known electrical,optical, or wireless connections.

The main computer that effects many aspects of the inventions willtypically operate in a networked environment using logical connectionsto one or more remote computers or data sources, which are describedfurther below. Remote computers may be another personal computer, aserver, a router, a network PC, a peer device or other common networknode, and typically include many or all of the elements described aboverelative to the main computer system in which the inventions areembodied. The logical connections between computers include a local areanetwork (LAN), a wide area network (WAN), and wireless LANs (WLAN) thatare presented here by way of example and not limitation. Such networkingenvironments are commonplace in office-wide or enterprise-wide computernetworks, intranets and the Internet.

When used in a LAN or WLAN networking environment, the main computersystem implementing aspects of the invention is connected to the localnetwork through a network interface or adapter. When used in a WAN orWLAN networking environment, the computer may include a modem, awireless link, or other mechanisms for establishing communications overthe wide area network, such as the Internet. In a networked environment,program modules depicted relative to the computer, or portions thereof,may be stored in a remote memory storage device. It will be appreciatedthat the network connections described or shown are exemplary and othermechanisms of establishing communications over wide area networks or theInternet may be used.

In view of the foregoing detailed description of preferred embodimentsof the present invention, it readily will be understood by those personsskilled in the art that the present invention is susceptible to broadutility and application. While various aspects have been described inthe context of a preferred embodiment, additional aspects, features, andmethodologies of the present invention will be readily discernible fromthe description herein, by those of ordinary skill in the art. Manyembodiments and adaptations of the present invention other than thoseherein described, as well as many variations, modifications, andequivalent arrangements and methodologies, will be apparent from orreasonably suggested by the present invention and the foregoingdescription thereof, without departing from the substance or scope ofthe present invention. Furthermore, any sequence(s) and/or temporalorder of steps of various processes described and claimed herein arethose considered to be the best mode contemplated for carrying out thepresent invention. It should also be understood that, although steps ofvarious processes may be shown and described as being in a preferredsequence or temporal order, the steps of any such processes are notlimited to being carried out in any particular sequence or order, absenta specific indication of such to achieve a particular intended result.In most cases, the steps of such processes may be carried out in avariety of different sequences and orders, while still falling withinthe scope of the present inventions. In addition, some steps may becarried out simultaneously.

The embodiments were chosen and described in order to explain theprinciples of the inventions and their practical application so as toenable others skilled in the art to utilize the inventions and variousembodiments and with various modifications as are suited to theparticular use contemplated. Alternative embodiments will becomeapparent to those skilled in the art to which the present inventionspertain without departing from their spirit and scope. Accordingly, thescope of the present inventions is defined by the appended claims ratherthan the foregoing description and the exemplary embodiments describedtherein.

What is claimed is:
 1. A method for evaluating, monitoring, ordiagnosing a mental disorder in an individual using an eye monitoringdevice, comprising the steps of: receiving blink data indicative of theindividual's blink responses to a stimulus, wherein the blink data iscollected via the eye monitoring device; synchronizing, via softwareexecuting on a processor, the received blink data with the stimulus;identifying, via software executing on the processor, a pattern of blinkinhibition in the synchronized blink data, wherein the pattern of blinkinhibition is identified via a comparison between the received blinkdata and a chance probability of blinking specific to the individualthat is derived from testing; retrieving, via software executing on theprocessor, event data related to the visual stimulus from a database;and comparing, via software executing on the processor, a parameter ofthe pattern of blink inhibition in the synchronized blink data with aparameter of the event data related to the visual stimulus to determinea delta parameter, wherein the delta parameter indicates a likelihoodthat the individual has a mental disorder.
 2. The method of claim 1,further comprising the steps of: receiving eye-movement data indicativeof the individual's eye movements with respect to the stimulus;receiving eye-movement data indicative of each member of a controlgroup's eye movements with respect to the stimulus; generating, viasoftware executing on a processor, a three-dimensional scanpath based onthe data for each of the members of the control group and for theindividual, wherein two of the dimensions of the scanpath correspond toa position of a point of regard for each of the members and theindividual and one of the dimensions corresponds to time; identifying,via software executing on the processor, a convergence of the scanpathsof the members of the control group; and comparing, via softwareexecuting on the processor, the scanpath of the individual to thescanpaths of the members of the control group in the region of theconvergence.
 3. The method of claim 1, wherein the parameter of theevent data comprises a predetermined time-stamped event.
 4. The methodof claim 1, wherein the parameter of the event data comprises a timevalue.
 5. The method of claim 1, wherein the parameter of the pattern ofblink inhibition comprises a time value.
 6. The method of claim 1,wherein the delta parameter comprises a time value that exceeds apredetermined threshold value.
 7. The method of claim 1, wherein thedelta parameter comprises a time value that is less than a predeterminedthreshold value.
 8. The method of claim 1, further comprising the stepof providing a diagnosis to the individual based on at least one deltaparameter.
 9. The method of claim 1, wherein the event data correspondsto one or more of the following: physical events within a dynamic visualstimulus, affective events within the dynamic visual stimulus, eventspresumed to cause or inhibit blinking based on the dynamic visualstimulus.
 10. The method of claim 1, wherein the pattern of blinkinhibition comprises a measure of an instantaneous blink rate for theindividual as compared to measure of variance in a mean blink rate forthe individual.
 11. A system for evaluating, monitoring, or diagnosing amental disorder in an individual using an eye monitoring device,comprising: a processor; software executing on the processor forreceiving blink data indicative of the individual's blink responses to astimulus, wherein the blink data is collected via the eye monitoringdevice; software executing on the processor for synchronizing thereceived blink data with the stimulus; software executing on theprocessor for identifying a pattern of blink inhibition in thesynchronized blink data, wherein the pattern of blink inhibition isidentified via a comparison between the received blink data and a chanceprobability of blinking specific to the individual that is derived fromtesting; software executing on the processor for retrieving event datarelated to the visual stimulus from a database; and software executingon the processor for comparing a parameter of the pattern of blinkinhibition in the synchronized blink data with a parameter of the eventdata related to the visual stimulus to determine a delta parameter,wherein the delta parameter indicates a likelihood that the individualhas a mental disorder.
 12. A method for evaluating, monitoring, ordiagnosing a mental disorder in an individual using an eye monitoringdevice, comprising the steps of: receiving blink data indicative of theindividual's blink responses to a stimulus, wherein the blink data iscollected using the eye monitoring device; synchronizing, via softwareexecuting on a processor, the received blink data with the stimulus;identifying, via software executing on the processor, a pattern of blinkinhibition in the synchronized blink data, wherein the pattern of blinkinhibition is identified via a comparison between the received blinkdata and a chance probability of blinking specific to the individualthat is derived from testing; retrieving, via software executing on theprocessor, a control pattern of blink inhibition for the stimulusdisplayed to the individual from a database; and comparing, via softwareexecuting on the processor, the pattern of blink inhibition in thesynchronized blink data with the control pattern of blink inhibition todetermine whether the pattern of blink inhibition falls outside a rangeof the control pattern of blink inhibition and thereby indicates alikelihood that the individual has a mental disorder.
 13. The method ofclaim 12, wherein the control pattern of blink inhibition comprises anaverage blink rate for a plurality of individuals in response to thestimulus.
 14. The method of claim 12, wherein the control pattern ofblink inhibition comprises a probability distribution of average blinkrates for a plurality of individuals as obtained by permuting the blinkdata of the plurality of individuals.
 15. The method of claim 14,wherein the step of permuting the data of the plurality of individualscomprises circular shifting with respect to an original timing of blinkdata collection.
 16. The method of claim 14, wherein the step ofpermuting the data of the plurality of individuals comprises randomizingan order of blinks and inter-blink intervals for each individual. 17.The method of claim 12, wherein the control pattern of blink inhibitioncomprises an average blink rate for the individual when no stimulus ispresent.
 18. The method of claim 12, wherein the control pattern ofblink inhibition indicates a severity of the mental condition.
 19. Asystem for evaluating, monitoring, or diagnosing a mental disorder in anindividual using an eye monitoring device, comprising: a processor;software executing on the processor for receiving blink data indicativeof the individual's blink responses to a stimulus, wherein the blinkdata is collected using the eye monitoring device; software executing onthe processor for synchronizing the received blink data with thestimulus; software executing on the processor for identifying a patternof blink inhibition in the synchronized blink data, wherein the patternof blink inhibition is identified via a comparison between the receivedblink data and a chance probability of blinking specific to theindividual that is derived from testing; software executing on theprocessor for retrieving a control pattern of blink inhibition for thestimulus displayed to the individual from a database; and softwareexecuting on the processor for comparing the pattern of blink inhibitionin the synchronized blink data with the control pattern of blinkinhibition to determine whether the pattern of blink inhibition fallsoutside a range of the control pattern of blink inhibition and therebyindicates a likelihood that the individual has a mental disorder.